| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | /* | ||
| 2 | - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - | ||
| 3 | SLEPc - Scalable Library for Eigenvalue Problem Computations | ||
| 4 | Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain | ||
| 5 | |||
| 6 | This file is part of SLEPc. | ||
| 7 | SLEPc is distributed under a 2-clause BSD license (see LICENSE). | ||
| 8 | - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - | ||
| 9 | */ | ||
| 10 | /* | ||
| 11 | SLEPc singular value solver: "trlanczos" | ||
| 12 | |||
| 13 | Method: Thick-restart Lanczos | ||
| 14 | |||
| 15 | Algorithm: | ||
| 16 | |||
| 17 | Golub-Kahan-Lanczos bidiagonalization with thick-restart. | ||
| 18 | |||
| 19 | References: | ||
| 20 | |||
| 21 | [1] G.H. Golub and W. Kahan, "Calculating the singular values | ||
| 22 | and pseudo-inverse of a matrix", SIAM J. Numer. Anal. Ser. | ||
| 23 | B 2:205-224, 1965. | ||
| 24 | |||
| 25 | [2] V. Hernandez, J.E. Roman, and A. Tomas, "A robust and | ||
| 26 | efficient parallel SVD solver based on restarted Lanczos | ||
| 27 | bidiagonalization", Elec. Trans. Numer. Anal. 31:68-85, | ||
| 28 | 2008. | ||
| 29 | */ | ||
| 30 | |||
| 31 | #include <slepc/private/svdimpl.h> /*I "slepcsvd.h" I*/ | ||
| 32 | #include <slepc/private/bvimpl.h> | ||
| 33 | |||
| 34 | static PetscBool cited = PETSC_FALSE,citedg = PETSC_FALSE; | ||
| 35 | static const char citation[] = | ||
| 36 | "@Article{slepc-svd,\n" | ||
| 37 | " author = \"V. Hern{\\'a}ndez and J. E. Rom{\\'a}n and A. Tom{\\'a}s\",\n" | ||
| 38 | " title = \"A robust and efficient parallel {SVD} solver based on restarted {Lanczos} bidiagonalization\",\n" | ||
| 39 | " journal = \"Electron. Trans. Numer. Anal.\",\n" | ||
| 40 | " volume = \"31\",\n" | ||
| 41 | " pages = \"68--85\",\n" | ||
| 42 | " year = \"2008\"\n" | ||
| 43 | "}\n"; | ||
| 44 | static const char citationg[] = | ||
| 45 | "@Article{slepc-gsvd,\n" | ||
| 46 | " author = \"F. Alvarruiz and C. Campos and J. E. Roman\",\n" | ||
| 47 | " title = \"Thick-restarted {Lanczos} bidiagonalization methods for the {GSVD}\",\n" | ||
| 48 | " journal = \"J. Comput. Appl. Math.\",\n" | ||
| 49 | " volume = \"440\",\n" | ||
| 50 | " pages = \"115506\",\n" | ||
| 51 | " year = \"2024\"\n" | ||
| 52 | "}\n"; | ||
| 53 | |||
| 54 | typedef struct { | ||
| 55 | /* user parameters */ | ||
| 56 | PetscBool oneside; /* one-sided variant */ | ||
| 57 | PetscReal keep; /* restart parameter */ | ||
| 58 | PetscBool lock; /* locking/non-locking variant */ | ||
| 59 | KSP ksp; /* solver for least-squares problem in GSVD */ | ||
| 60 | SVDTRLanczosGBidiag bidiag; /* bidiagonalization variant for GSVD */ | ||
| 61 | PetscReal scalef; /* scale factor for matrix B */ | ||
| 62 | PetscReal scaleth; /* scale threshold for automatic scaling */ | ||
| 63 | PetscBool explicitmatrix; | ||
| 64 | /* auxiliary variables */ | ||
| 65 | Mat Z; /* aux matrix for GSVD, Z=[A;B] */ | ||
| 66 | } SVD_TRLANCZOS; | ||
| 67 | |||
| 68 | /* Context for shell matrix [A; B] */ | ||
| 69 | typedef struct { | ||
| 70 | Mat A,B,AT,BT; | ||
| 71 | Vec y1,y2,y; | ||
| 72 | PetscInt m; | ||
| 73 | PetscReal scalef; | ||
| 74 | } MatZData; | ||
| 75 | |||
| 76 | 430 | static PetscErrorCode MatZCreateContext(SVD svd,MatZData **zdata) | |
| 77 | { | ||
| 78 | 430 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 79 | |||
| 80 |
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430 | PetscFunctionBegin; |
| 81 |
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430 | PetscCall(PetscNew(zdata)); |
| 82 | 430 | (*zdata)->A = svd->A; | |
| 83 | 430 | (*zdata)->B = svd->B; | |
| 84 | 430 | (*zdata)->AT = svd->AT; | |
| 85 | 430 | (*zdata)->BT = svd->BT; | |
| 86 | 430 | (*zdata)->scalef = lanczos->scalef; | |
| 87 |
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430 | PetscCall(MatCreateVecsEmpty(svd->A,NULL,&(*zdata)->y1)); |
| 88 |
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430 | PetscCall(MatCreateVecsEmpty(svd->B,NULL,&(*zdata)->y2)); |
| 89 |
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430 | PetscCall(VecGetLocalSize((*zdata)->y1,&(*zdata)->m)); |
| 90 |
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430 | PetscCall(BVCreateVec(svd->U,&(*zdata)->y)); |
| 91 |
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106 | PetscFunctionReturn(PETSC_SUCCESS); |
| 92 | } | ||
| 93 | |||
| 94 | /* Update scale factor for B in Z=[A;B] | ||
| 95 | If matrices are swapped, the scale factor is inverted.*/ | ||
| 96 | 581 | static PetscErrorCode MatZUpdateScale(SVD svd) | |
| 97 | { | ||
| 98 | 581 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 99 | 581 | MatZData *zdata; | |
| 100 | 581 | Mat mats[2],normal; | |
| 101 | 581 | MatType Atype; | |
| 102 | 581 | PetscBool sametype; | |
| 103 |
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581 | PetscReal scalef = svd->swapped? 1.0/lanczos->scalef : lanczos->scalef; |
| 104 | |||
| 105 |
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581 | PetscFunctionBegin; |
| 106 |
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581 | if (lanczos->explicitmatrix) { |
| 107 | /* Destroy the matrix Z and create it again */ | ||
| 108 |
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99 | PetscCall(MatDestroy(&lanczos->Z)); |
| 109 | 99 | mats[0] = svd->A; | |
| 110 |
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99 | if (scalef == 1.0) { |
| 111 | 37 | mats[1] = svd->B; | |
| 112 | } else { | ||
| 113 |
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62 | PetscCall(MatDuplicate(svd->B,MAT_COPY_VALUES,&mats[1])); |
| 114 |
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62 | PetscCall(MatScale(mats[1],scalef)); |
| 115 | } | ||
| 116 |
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99 | PetscCall(MatCreateNest(PetscObjectComm((PetscObject)svd),2,NULL,1,NULL,mats,&lanczos->Z)); |
| 117 |
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99 | PetscCall(MatGetType(svd->A,&Atype)); |
| 118 |
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99 | PetscCall(PetscObjectTypeCompare((PetscObject)svd->B,Atype,&sametype)); |
| 119 |
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99 | if (!sametype) Atype = MATAIJ; |
| 120 |
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99 | PetscCall(MatConvert(lanczos->Z,Atype,MAT_INPLACE_MATRIX,&lanczos->Z)); |
| 121 |
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99 | if (scalef != 1.0) PetscCall(MatDestroy(&mats[1])); |
| 122 | } else { | ||
| 123 |
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482 | PetscCall(MatShellGetContext(lanczos->Z,&zdata)); |
| 124 | 482 | zdata->scalef = scalef; | |
| 125 | } | ||
| 126 | |||
| 127 | /* create normal equations matrix, to build the preconditioner in LSQR */ | ||
| 128 |
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581 | PetscCall(MatCreateNormalHermitian(lanczos->Z,&normal)); |
| 129 | |||
| 130 |
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581 | if (!lanczos->ksp) PetscCall(SVDTRLanczosGetKSP(svd,&lanczos->ksp)); |
| 131 |
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581 | PetscCall(SVD_KSPSetOperators(lanczos->ksp,lanczos->Z,normal)); |
| 132 |
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581 | PetscCall(KSPSetUp(lanczos->ksp)); |
| 133 |
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581 | PetscCall(MatDestroy(&normal)); |
| 134 |
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147 | PetscFunctionReturn(PETSC_SUCCESS); |
| 135 | } | ||
| 136 | |||
| 137 | 430 | static PetscErrorCode MatDestroy_Z(Mat Z) | |
| 138 | { | ||
| 139 | 430 | MatZData *zdata; | |
| 140 | |||
| 141 |
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430 | PetscFunctionBegin; |
| 142 |
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430 | PetscCall(MatShellGetContext(Z,&zdata)); |
| 143 |
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430 | PetscCall(VecDestroy(&zdata->y1)); |
| 144 |
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430 | PetscCall(VecDestroy(&zdata->y2)); |
| 145 |
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430 | PetscCall(VecDestroy(&zdata->y)); |
| 146 |
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430 | PetscCall(PetscFree(zdata)); |
| 147 |
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106 | PetscFunctionReturn(PETSC_SUCCESS); |
| 148 | } | ||
| 149 | |||
| 150 | 912035 | static PetscErrorCode MatMult_Z(Mat Z,Vec x,Vec y) | |
| 151 | { | ||
| 152 | 912035 | MatZData *zdata; | |
| 153 | 912035 | PetscScalar *y_elems; | |
| 154 | |||
| 155 |
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912035 | PetscFunctionBegin; |
| 156 |
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912035 | PetscCall(MatShellGetContext(Z,&zdata)); |
| 157 |
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912035 | PetscCall(VecGetArray(y,&y_elems)); |
| 158 |
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912035 | PetscCall(VecPlaceArray(zdata->y1,y_elems)); |
| 159 |
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912035 | PetscCall(VecPlaceArray(zdata->y2,y_elems+zdata->m)); |
| 160 | |||
| 161 |
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912035 | PetscCall(MatMult(zdata->A,x,zdata->y1)); |
| 162 |
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912035 | PetscCall(MatMult(zdata->B,x,zdata->y2)); |
| 163 |
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912035 | PetscCall(VecScale(zdata->y2,zdata->scalef)); |
| 164 | |||
| 165 |
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912035 | PetscCall(VecResetArray(zdata->y1)); |
| 166 |
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912035 | PetscCall(VecResetArray(zdata->y2)); |
| 167 |
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912035 | PetscCall(VecRestoreArray(y,&y_elems)); |
| 168 |
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230019 | PetscFunctionReturn(PETSC_SUCCESS); |
| 169 | } | ||
| 170 | |||
| 171 | 913736 | static PetscErrorCode MatMultTranspose_Z(Mat Z,Vec y,Vec x) | |
| 172 | { | ||
| 173 | 913736 | MatZData *zdata; | |
| 174 | 913736 | const PetscScalar *y_elems; | |
| 175 | |||
| 176 |
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913736 | PetscFunctionBegin; |
| 177 |
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913736 | PetscCall(MatShellGetContext(Z,&zdata)); |
| 178 |
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913736 | PetscCall(VecGetArrayRead(y,&y_elems)); |
| 179 |
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913736 | PetscCall(VecPlaceArray(zdata->y1,y_elems)); |
| 180 |
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913736 | PetscCall(VecPlaceArray(zdata->y2,y_elems+zdata->m)); |
| 181 | |||
| 182 |
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913736 | PetscCall(MatMult(zdata->BT,zdata->y2,x)); |
| 183 |
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913736 | PetscCall(VecScale(x,zdata->scalef)); |
| 184 |
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913736 | PetscCall(MatMultAdd(zdata->AT,zdata->y1,x,x)); |
| 185 | |||
| 186 |
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913736 | PetscCall(VecResetArray(zdata->y1)); |
| 187 |
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913736 | PetscCall(VecResetArray(zdata->y2)); |
| 188 |
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913736 | PetscCall(VecRestoreArrayRead(y,&y_elems)); |
| 189 |
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230437 | PetscFunctionReturn(PETSC_SUCCESS); |
| 190 | } | ||
| 191 | |||
| 192 | 1238 | static PetscErrorCode MatCreateVecs_Z(Mat Z,Vec *right,Vec *left) | |
| 193 | { | ||
| 194 | 1238 | MatZData *zdata; | |
| 195 | |||
| 196 |
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1238 | PetscFunctionBegin; |
| 197 |
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1238 | PetscCall(MatShellGetContext(Z,&zdata)); |
| 198 |
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1238 | if (right) PetscCall(MatCreateVecs(zdata->A,right,NULL)); |
| 199 |
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1238 | if (left) PetscCall(VecDuplicate(zdata->y,left)); |
| 200 |
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307 | PetscFunctionReturn(PETSC_SUCCESS); |
| 201 | } | ||
| 202 | |||
| 203 | 864 | static PetscErrorCode SVDSetUp_TRLanczos(SVD svd) | |
| 204 | { | ||
| 205 | 864 | PetscInt M,N,P,m,n,p; | |
| 206 | 864 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 207 | 864 | MatZData *zdata; | |
| 208 | 864 | Mat aux; | |
| 209 | |||
| 210 |
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864 | PetscFunctionBegin; |
| 211 |
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864 | PetscCall(MatGetSize(svd->A,&M,&N)); |
| 212 |
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864 | PetscCall(SVDSetDimensions_Default(svd)); |
| 213 |
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864 | PetscCheck(svd->ncv<=svd->nsv+svd->mpd,PetscObjectComm((PetscObject)svd),PETSC_ERR_USER_INPUT,"The value of ncv must not be larger than nsv+mpd"); |
| 214 |
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864 | PetscCheck(lanczos->lock || svd->mpd>=svd->ncv,PetscObjectComm((PetscObject)svd),PETSC_ERR_SUP,"Should not use mpd parameter in non-locking variant"); |
| 215 |
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1534 | if (svd->max_it==PETSC_DETERMINE) svd->max_it = PetscMax(N/svd->ncv,100)*((svd->stop==SVD_STOP_THRESHOLD)?10:1); |
| 216 |
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864 | if (!lanczos->keep) lanczos->keep = 0.5; |
| 217 | 864 | svd->leftbasis = PETSC_TRUE; | |
| 218 |
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864 | PetscCall(SVDAllocateSolution(svd,1)); |
| 219 |
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864 | if (svd->isgeneralized) { |
| 220 |
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497 | PetscCall(MatGetSize(svd->B,&P,NULL)); |
| 221 |
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497 | if (lanczos->bidiag == SVD_TRLANCZOS_GBIDIAG_LOWER && ((svd->which==SVD_LARGEST && P<=N) || (svd->which==SVD_SMALLEST && M>N && P<=N))) { |
| 222 | 33 | SlepcSwap(svd->A,svd->B,aux); | |
| 223 | 33 | SlepcSwap(svd->AT,svd->BT,aux); | |
| 224 | 33 | svd->swapped = PETSC_TRUE; | |
| 225 | 464 | } else svd->swapped = PETSC_FALSE; | |
| 226 | |||
| 227 |
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497 | PetscCall(SVDSetWorkVecs(svd,1,1)); |
| 228 | |||
| 229 |
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497 | if (svd->conv==SVD_CONV_ABS) { /* Residual norms are multiplied by matrix norms */ |
| 230 | ✗ | if (!svd->nrma) PetscCall(MatNorm(svd->A,NORM_INFINITY,&svd->nrma)); | |
| 231 | ✗ | if (!svd->nrmb) PetscCall(MatNorm(svd->B,NORM_INFINITY,&svd->nrmb)); | |
| 232 | } | ||
| 233 | |||
| 234 | /* Create the matrix Z=[A;B] */ | ||
| 235 |
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497 | PetscCall(MatGetLocalSize(svd->A,&m,&n)); |
| 236 |
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497 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 237 |
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497 | if (!lanczos->explicitmatrix) { |
| 238 |
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430 | PetscCall(MatDestroy(&lanczos->Z)); |
| 239 |
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430 | PetscCall(MatZCreateContext(svd,&zdata)); |
| 240 |
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430 | PetscCall(MatCreateShell(PetscObjectComm((PetscObject)svd),m+p,n,PETSC_DECIDE,PETSC_DECIDE,zdata,&lanczos->Z)); |
| 241 |
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430 | PetscCall(MatShellSetOperation(lanczos->Z,MATOP_MULT,(PetscErrorCodeFn*)MatMult_Z)); |
| 242 | #if defined(PETSC_USE_COMPLEX) | ||
| 243 |
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206 | PetscCall(MatShellSetOperation(lanczos->Z,MATOP_MULT_HERMITIAN_TRANSPOSE,(PetscErrorCodeFn*)MatMultTranspose_Z)); |
| 244 | #else | ||
| 245 |
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224 | PetscCall(MatShellSetOperation(lanczos->Z,MATOP_MULT_TRANSPOSE,(PetscErrorCodeFn*)MatMultTranspose_Z)); |
| 246 | #endif | ||
| 247 |
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430 | PetscCall(MatShellSetOperation(lanczos->Z,MATOP_CREATE_VECS,(PetscErrorCodeFn*)MatCreateVecs_Z)); |
| 248 |
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430 | PetscCall(MatShellSetOperation(lanczos->Z,MATOP_DESTROY,(PetscErrorCodeFn*)MatDestroy_Z)); |
| 249 | } | ||
| 250 | /* Explicit matrix is created here, when updating the scale */ | ||
| 251 |
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497 | PetscCall(MatZUpdateScale(svd)); |
| 252 | |||
| 253 |
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367 | } else if (svd->ishyperbolic) { |
| 254 |
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97 | PetscCall(BV_SetMatrixDiagonal(svd->swapped?svd->V:svd->U,svd->omega,svd->OP)); |
| 255 |
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97 | PetscCall(SVDSetWorkVecs(svd,1,0)); |
| 256 | } | ||
| 257 |
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864 | PetscCall(DSSetCompact(svd->ds,PETSC_TRUE)); |
| 258 |
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864 | PetscCall(DSSetExtraRow(svd->ds,PETSC_TRUE)); |
| 259 |
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864 | PetscCall(DSAllocate(svd->ds,svd->ncv+1)); |
| 260 |
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216 | PetscFunctionReturn(PETSC_SUCCESS); |
| 261 | } | ||
| 262 | |||
| 263 | 182 | static PetscErrorCode SVDOneSideTRLanczosMGS(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,BV U,PetscInt nconv,PetscInt l,PetscInt n,PetscScalar* work) | |
| 264 | { | ||
| 265 | 182 | PetscReal a,b; | |
| 266 | 182 | PetscInt i,k=nconv+l; | |
| 267 | 182 | Vec ui,ui1,vi; | |
| 268 | |||
| 269 |
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182 | PetscFunctionBegin; |
| 270 |
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182 | PetscCall(BVGetColumn(V,k,&vi)); |
| 271 |
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182 | PetscCall(BVGetColumn(U,k,&ui)); |
| 272 |
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182 | PetscCall(MatMult(svd->A,vi,ui)); |
| 273 |
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182 | PetscCall(BVRestoreColumn(V,k,&vi)); |
| 274 |
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182 | PetscCall(BVRestoreColumn(U,k,&ui)); |
| 275 |
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182 | if (l>0) { |
| 276 |
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164 | PetscCall(BVSetActiveColumns(U,nconv,n)); |
| 277 |
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930 | for (i=0;i<l;i++) work[i]=beta[i+nconv]; |
| 278 |
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164 | PetscCall(BVMultColumn(U,-1.0,1.0,k,work)); |
| 279 | } | ||
| 280 |
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182 | PetscCall(BVNormColumn(U,k,NORM_2,&a)); |
| 281 |
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182 | PetscCall(BVScaleColumn(U,k,1.0/a)); |
| 282 | 182 | alpha[k] = a; | |
| 283 | |||
| 284 |
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946 | for (i=k+1;i<n;i++) { |
| 285 |
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764 | PetscCall(BVGetColumn(V,i,&vi)); |
| 286 |
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764 | PetscCall(BVGetColumn(U,i-1,&ui1)); |
| 287 |
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764 | PetscCall(MatMult(svd->AT,ui1,vi)); |
| 288 |
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764 | PetscCall(BVRestoreColumn(V,i,&vi)); |
| 289 |
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764 | PetscCall(BVRestoreColumn(U,i-1,&ui1)); |
| 290 |
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764 | PetscCall(BVOrthonormalizeColumn(V,i,PETSC_FALSE,&b,NULL)); |
| 291 | 764 | beta[i-1] = b; | |
| 292 | |||
| 293 |
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764 | PetscCall(BVGetColumn(V,i,&vi)); |
| 294 |
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764 | PetscCall(BVGetColumn(U,i,&ui)); |
| 295 |
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764 | PetscCall(MatMult(svd->A,vi,ui)); |
| 296 |
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764 | PetscCall(BVRestoreColumn(V,i,&vi)); |
| 297 |
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764 | PetscCall(BVGetColumn(U,i-1,&ui1)); |
| 298 |
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764 | PetscCall(VecAXPY(ui,-b,ui1)); |
| 299 |
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764 | PetscCall(BVRestoreColumn(U,i-1,&ui1)); |
| 300 |
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764 | PetscCall(BVRestoreColumn(U,i,&ui)); |
| 301 |
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764 | PetscCall(BVNormColumn(U,i,NORM_2,&a)); |
| 302 |
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764 | PetscCall(BVScaleColumn(U,i,1.0/a)); |
| 303 | 764 | alpha[i] = a; | |
| 304 | } | ||
| 305 | |||
| 306 |
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182 | PetscCall(BVGetColumn(V,n,&vi)); |
| 307 |
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182 | PetscCall(BVGetColumn(U,n-1,&ui1)); |
| 308 |
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182 | PetscCall(MatMult(svd->AT,ui1,vi)); |
| 309 |
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182 | PetscCall(BVRestoreColumn(V,n,&vi)); |
| 310 |
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182 | PetscCall(BVRestoreColumn(U,n-1,&ui1)); |
| 311 |
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182 | PetscCall(BVOrthogonalizeColumn(V,n,NULL,&b,NULL)); |
| 312 | 182 | beta[n-1] = b; | |
| 313 |
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182 | PetscFunctionReturn(PETSC_SUCCESS); |
| 314 | } | ||
| 315 | |||
| 316 | /* | ||
| 317 | Custom CGS orthogonalization, preprocess after first orthogonalization | ||
| 318 | */ | ||
| 319 | 1892 | static PetscErrorCode SVDOrthogonalizeCGS(BV V,PetscInt i,PetscScalar* h,PetscReal a,BVOrthogRefineType refine,PetscReal eta,PetscReal *norm) | |
| 320 | { | ||
| 321 | 1892 | PetscReal sum,onorm; | |
| 322 | 1892 | PetscScalar dot; | |
| 323 | 1892 | PetscInt j; | |
| 324 | |||
| 325 |
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1892 | PetscFunctionBegin; |
| 326 |
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1892 | switch (refine) { |
| 327 | ✗ | case BV_ORTHOG_REFINE_NEVER: | |
| 328 | ✗ | PetscCall(BVNormColumn(V,i,NORM_2,norm)); | |
| 329 | break; | ||
| 330 | 946 | case BV_ORTHOG_REFINE_ALWAYS: | |
| 331 |
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946 | PetscCall(BVSetActiveColumns(V,0,i)); |
| 332 |
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946 | PetscCall(BVDotColumn(V,i,h)); |
| 333 |
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946 | PetscCall(BVMultColumn(V,-1.0,1.0,i,h)); |
| 334 |
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946 | PetscCall(BVNormColumn(V,i,NORM_2,norm)); |
| 335 | break; | ||
| 336 | 946 | case BV_ORTHOG_REFINE_IFNEEDED: | |
| 337 | 946 | dot = h[i]; | |
| 338 | 946 | onorm = PetscSqrtReal(PetscRealPart(dot)) / a; | |
| 339 | 946 | sum = 0.0; | |
| 340 |
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8172 | for (j=0;j<i;j++) { |
| 341 | 7226 | sum += PetscRealPart(h[j] * PetscConj(h[j])); | |
| 342 | } | ||
| 343 | 946 | *norm = PetscRealPart(dot)/(a*a) - sum; | |
| 344 |
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946 | if (*norm>0.0) *norm = PetscSqrtReal(*norm); |
| 345 | ✗ | else PetscCall(BVNormColumn(V,i,NORM_2,norm)); | |
| 346 |
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|
946 | if (*norm < eta*onorm) { |
| 347 |
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946 | PetscCall(BVSetActiveColumns(V,0,i)); |
| 348 |
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946 | PetscCall(BVDotColumn(V,i,h)); |
| 349 |
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946 | PetscCall(BVMultColumn(V,-1.0,1.0,i,h)); |
| 350 |
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946 | PetscCall(BVNormColumn(V,i,NORM_2,norm)); |
| 351 | } | ||
| 352 | break; | ||
| 353 | } | ||
| 354 |
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|
416 | PetscFunctionReturn(PETSC_SUCCESS); |
| 355 | } | ||
| 356 | |||
| 357 | 364 | static PetscErrorCode SVDOneSideTRLanczosCGS(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,BV U,PetscInt nconv,PetscInt l,PetscInt n,PetscScalar* work) | |
| 358 | { | ||
| 359 | 364 | PetscReal a,b,eta; | |
| 360 | 364 | PetscInt i,j,k=nconv+l; | |
| 361 | 364 | Vec ui,ui1,vi; | |
| 362 | 364 | BVOrthogRefineType refine; | |
| 363 | |||
| 364 |
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364 | PetscFunctionBegin; |
| 365 |
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364 | PetscCall(BVGetColumn(V,k,&vi)); |
| 366 |
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364 | PetscCall(BVGetColumn(U,k,&ui)); |
| 367 |
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364 | PetscCall(MatMult(svd->A,vi,ui)); |
| 368 |
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364 | PetscCall(BVRestoreColumn(V,k,&vi)); |
| 369 |
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364 | PetscCall(BVRestoreColumn(U,k,&ui)); |
| 370 |
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364 | if (l>0) { |
| 371 |
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328 | PetscCall(BVSetActiveColumns(U,nconv,n)); |
| 372 |
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|
1860 | for (i=0;i<l;i++) work[i]=beta[i+nconv]; |
| 373 |
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328 | PetscCall(BVMultColumn(U,-1.0,1.0,k,work)); |
| 374 | } | ||
| 375 |
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364 | PetscCall(BVGetOrthogonalization(V,NULL,&refine,&eta,NULL)); |
| 376 | |||
| 377 |
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1892 | for (i=k+1;i<n;i++) { |
| 378 |
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1528 | PetscCall(BVGetColumn(V,i,&vi)); |
| 379 |
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1528 | PetscCall(BVGetColumn(U,i-1,&ui1)); |
| 380 |
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1528 | PetscCall(MatMult(svd->AT,ui1,vi)); |
| 381 |
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1528 | PetscCall(BVRestoreColumn(V,i,&vi)); |
| 382 |
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1528 | PetscCall(BVRestoreColumn(U,i-1,&ui1)); |
| 383 |
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1528 | PetscCall(BVNormColumnBegin(U,i-1,NORM_2,&a)); |
| 384 |
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1528 | if (refine == BV_ORTHOG_REFINE_IFNEEDED) { |
| 385 |
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764 | PetscCall(BVSetActiveColumns(V,0,i+1)); |
| 386 |
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764 | PetscCall(BVGetColumn(V,i,&vi)); |
| 387 |
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764 | PetscCall(BVDotVecBegin(V,vi,work)); |
| 388 | } else { | ||
| 389 |
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764 | PetscCall(BVSetActiveColumns(V,0,i)); |
| 390 |
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764 | PetscCall(BVDotColumnBegin(V,i,work)); |
| 391 | } | ||
| 392 |
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1528 | PetscCall(BVNormColumnEnd(U,i-1,NORM_2,&a)); |
| 393 |
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1528 | if (refine == BV_ORTHOG_REFINE_IFNEEDED) { |
| 394 |
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764 | PetscCall(BVDotVecEnd(V,vi,work)); |
| 395 |
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764 | PetscCall(BVRestoreColumn(V,i,&vi)); |
| 396 |
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764 | PetscCall(BVSetActiveColumns(V,0,i)); |
| 397 |
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764 | } else PetscCall(BVDotColumnEnd(V,i,work)); |
| 398 | |||
| 399 |
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1528 | PetscCall(BVScaleColumn(U,i-1,1.0/a)); |
| 400 |
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12340 | for (j=0;j<i;j++) work[j] = work[j] / a; |
| 401 |
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1528 | PetscCall(BVMultColumn(V,-1.0,1.0/a,i,work)); |
| 402 |
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1528 | PetscCall(SVDOrthogonalizeCGS(V,i,work,a,refine,eta,&b)); |
| 403 |
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1528 | PetscCall(BVScaleColumn(V,i,1.0/b)); |
| 404 |
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1528 | PetscCheck(PetscAbsReal(b)>10*PETSC_MACHINE_EPSILON,PetscObjectComm((PetscObject)svd),PETSC_ERR_PLIB,"Recurrence generated a zero vector; use a two-sided variant"); |
| 405 | |||
| 406 |
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1528 | PetscCall(BVGetColumn(V,i,&vi)); |
| 407 |
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1528 | PetscCall(BVGetColumn(U,i,&ui)); |
| 408 |
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1528 | PetscCall(BVGetColumn(U,i-1,&ui1)); |
| 409 |
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1528 | PetscCall(MatMult(svd->A,vi,ui)); |
| 410 |
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1528 | PetscCall(VecAXPY(ui,-b,ui1)); |
| 411 |
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1528 | PetscCall(BVRestoreColumn(V,i,&vi)); |
| 412 |
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1528 | PetscCall(BVRestoreColumn(U,i,&ui)); |
| 413 |
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1528 | PetscCall(BVRestoreColumn(U,i-1,&ui1)); |
| 414 | |||
| 415 | 1528 | alpha[i-1] = a; | |
| 416 | 1528 | beta[i-1] = b; | |
| 417 | } | ||
| 418 | |||
| 419 |
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364 | PetscCall(BVGetColumn(V,n,&vi)); |
| 420 |
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364 | PetscCall(BVGetColumn(U,n-1,&ui1)); |
| 421 |
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364 | PetscCall(MatMult(svd->AT,ui1,vi)); |
| 422 |
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364 | PetscCall(BVRestoreColumn(V,n,&vi)); |
| 423 |
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364 | PetscCall(BVRestoreColumn(U,n-1,&ui1)); |
| 424 | |||
| 425 |
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364 | PetscCall(BVNormColumnBegin(svd->U,n-1,NORM_2,&a)); |
| 426 |
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364 | if (refine == BV_ORTHOG_REFINE_IFNEEDED) { |
| 427 |
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182 | PetscCall(BVSetActiveColumns(V,0,n+1)); |
| 428 |
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182 | PetscCall(BVGetColumn(V,n,&vi)); |
| 429 |
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182 | PetscCall(BVDotVecBegin(V,vi,work)); |
| 430 | } else { | ||
| 431 |
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182 | PetscCall(BVSetActiveColumns(V,0,n)); |
| 432 |
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182 | PetscCall(BVDotColumnBegin(V,n,work)); |
| 433 | } | ||
| 434 |
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364 | PetscCall(BVNormColumnEnd(svd->U,n-1,NORM_2,&a)); |
| 435 |
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364 | if (refine == BV_ORTHOG_REFINE_IFNEEDED) { |
| 436 |
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182 | PetscCall(BVDotVecEnd(V,vi,work)); |
| 437 |
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182 | PetscCall(BVRestoreColumn(V,n,&vi)); |
| 438 |
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182 | } else PetscCall(BVDotColumnEnd(V,n,work)); |
| 439 | |||
| 440 |
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364 | PetscCall(BVScaleColumn(U,n-1,1.0/a)); |
| 441 |
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4004 | for (j=0;j<n;j++) work[j] = work[j] / a; |
| 442 |
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364 | PetscCall(BVMultColumn(V,-1.0,1.0/a,n,work)); |
| 443 |
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364 | PetscCall(SVDOrthogonalizeCGS(V,n,work,a,refine,eta,&b)); |
| 444 |
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364 | PetscCall(BVSetActiveColumns(V,nconv,n)); |
| 445 | 364 | alpha[n-1] = a; | |
| 446 | 364 | beta[n-1] = b; | |
| 447 |
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364 | PetscFunctionReturn(PETSC_SUCCESS); |
| 448 | } | ||
| 449 | |||
| 450 | 270 | static PetscErrorCode SVDSolve_TRLanczos(SVD svd) | |
| 451 | { | ||
| 452 | 270 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 453 | 270 | PetscReal *alpha,*beta; | |
| 454 | 270 | PetscScalar *swork=NULL,*w,*aux; | |
| 455 | 270 | PetscInt i,k,l,nv,ld; | |
| 456 | 270 | Mat U,V; | |
| 457 | 270 | PetscBool breakdown=PETSC_FALSE; | |
| 458 | 270 | BVOrthogType orthog; | |
| 459 | |||
| 460 |
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270 | PetscFunctionBegin; |
| 461 |
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270 | PetscCall(PetscCitationsRegister(citation,&cited)); |
| 462 | /* allocate working space */ | ||
| 463 |
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270 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 464 |
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270 | PetscCall(BVGetOrthogonalization(svd->V,&orthog,NULL,NULL,NULL)); |
| 465 |
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270 | PetscCall(PetscMalloc1(ld,&w)); |
| 466 |
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270 | if (lanczos->oneside) PetscCall(PetscMalloc1(svd->ncv+1,&swork)); |
| 467 | |||
| 468 | /* normalize start vector */ | ||
| 469 |
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270 | if (!svd->nini) { |
| 470 |
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190 | PetscCall(BVSetRandomColumn(svd->V,0)); |
| 471 |
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190 | PetscCall(BVOrthonormalizeColumn(svd->V,0,PETSC_TRUE,NULL,NULL)); |
| 472 | } | ||
| 473 | |||
| 474 | l = 0; | ||
| 475 | 6253 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 476 | 5983 | svd->its++; | |
| 477 | |||
| 478 | /* inner loop */ | ||
| 479 | 5983 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 480 |
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5983 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 481 | 5983 | beta = alpha + ld; | |
| 482 |
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|
5983 | if (lanczos->oneside) { |
| 483 |
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|
546 | if (orthog == BV_ORTHOG_MGS) PetscCall(SVDOneSideTRLanczosMGS(svd,alpha,beta,svd->V,svd->U,svd->nconv,l,nv,swork)); |
| 484 |
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|
364 | else PetscCall(SVDOneSideTRLanczosCGS(svd,alpha,beta,svd->V,svd->U,svd->nconv,l,nv,swork)); |
| 485 |
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|
5437 | } else PetscCall(SVDTwoSideLanczos(svd,alpha,beta,svd->V,svd->U,svd->nconv+l,&nv,&breakdown)); |
| 486 |
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|
5983 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 487 |
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|
5983 | PetscCall(BVScaleColumn(svd->V,nv,1.0/beta[nv-1])); |
| 488 |
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|
5983 | PetscCall(BVSetActiveColumns(svd->V,svd->nconv,nv)); |
| 489 |
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|
5983 | PetscCall(BVSetActiveColumns(svd->U,svd->nconv,nv)); |
| 490 | |||
| 491 | /* solve projected problem */ | ||
| 492 |
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|
5983 | PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,svd->nconv+l)); |
| 493 |
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|
5983 | PetscCall(DSSVDSetDimensions(svd->ds,nv)); |
| 494 |
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|
5983 | PetscCall(DSSetState(svd->ds,l?DS_STATE_RAW:DS_STATE_INTERMEDIATE)); |
| 495 |
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|
5983 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 496 |
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|
5983 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 497 |
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|
5983 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 498 |
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|
5983 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 499 |
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|
65769 | for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]); |
| 500 | |||
| 501 | /* check convergence */ | ||
| 502 |
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|
5983 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,1.0,&k)); |
| 503 |
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|
5983 | SVDSetCtxThreshold(svd,svd->sigma,k); |
| 504 |
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|
5983 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 505 | |||
| 506 | /* update l */ | ||
| 507 |
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|
5983 | if (svd->reason != SVD_CONVERGED_ITERATING || breakdown || k==nv) l = 0; |
| 508 | 5713 | else l = PetscMax(1,(PetscInt)((nv-k)*lanczos->keep)); | |
| 509 |
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|
5983 | if (!lanczos->lock && l>0) { l += k; k = 0; } /* non-locking variant: reset no. of converged triplets */ |
| 510 |
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|
5983 | if (l) PetscCall(PetscInfo(svd,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l)); |
| 511 | |||
| 512 |
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5983 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 513 |
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|
5713 | if (PetscUnlikely(breakdown || k==nv)) { |
| 514 | /* Start a new bidiagonalization */ | ||
| 515 | ✗ | PetscCall(PetscInfo(svd,"Breakdown in bidiagonalization (it=%" PetscInt_FMT ")\n",svd->its)); | |
| 516 | ✗ | if (k<svd->nsv) { | |
| 517 | ✗ | PetscCall(BVSetRandomColumn(svd->V,k)); | |
| 518 | ✗ | PetscCall(BVOrthonormalizeColumn(svd->V,k,PETSC_FALSE,NULL,&breakdown)); | |
| 519 | ✗ | if (breakdown) { | |
| 520 | ✗ | svd->reason = SVD_DIVERGED_BREAKDOWN; | |
| 521 | ✗ | PetscCall(PetscInfo(svd,"Unable to generate more start vectors\n")); | |
| 522 | } | ||
| 523 | } | ||
| 524 |
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|
5713 | } else PetscCall(DSTruncate(svd->ds,k+l,PETSC_FALSE)); |
| 525 | } | ||
| 526 | |||
| 527 | /* compute converged singular vectors and restart vectors */ | ||
| 528 |
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5983 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&V)); |
| 529 |
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5983 | PetscCall(BVMultInPlace(svd->V,V,svd->nconv,k+l)); |
| 530 |
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5983 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&V)); |
| 531 |
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5983 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U)); |
| 532 |
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5983 | PetscCall(BVMultInPlace(svd->U,U,svd->nconv,k+l)); |
| 533 |
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5983 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U)); |
| 534 | |||
| 535 |
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5983 | if (svd->reason == SVD_CONVERGED_ITERATING && !breakdown) { |
| 536 |
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5713 | PetscCall(BVCopyColumn(svd->V,nv,k+l)); /* copy the last vector to be the next initial vector */ |
| 537 |
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|
5713 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 538 | 3 | svd->ncv = svd->mpd+k; | |
| 539 |
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3 | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); |
| 540 |
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21 | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; |
| 541 |
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3 | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); |
| 542 | 3 | aux = w; | |
| 543 |
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3 | PetscCall(PetscMalloc1(svd->ncv+1,&w)); |
| 544 |
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3 | PetscCall(PetscArraycpy(w,aux,ld)); |
| 545 |
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3 | PetscCall(PetscFree(aux)); |
| 546 |
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3 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 547 |
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3 | if (lanczos->oneside) { |
| 548 | ✗ | PetscCall(PetscFree(swork)); | |
| 549 | ✗ | PetscCall(PetscMalloc1(svd->ncv+1,&swork)); | |
| 550 | } | ||
| 551 | } | ||
| 552 | } | ||
| 553 | |||
| 554 | 5983 | svd->nconv = k; | |
| 555 |
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|
6253 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,nv)); |
| 556 | } | ||
| 557 | |||
| 558 | /* orthonormalize U columns in one side method */ | ||
| 559 |
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270 | if (lanczos->oneside) { |
| 560 |
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|
294 | for (i=0;i<svd->nconv;i++) PetscCall(BVOrthonormalizeColumn(svd->U,i,PETSC_FALSE,NULL,NULL)); |
| 561 | } | ||
| 562 | |||
| 563 | /* free working space */ | ||
| 564 |
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270 | PetscCall(PetscFree(w)); |
| 565 |
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270 | if (swork) PetscCall(PetscFree(swork)); |
| 566 |
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270 | PetscCall(DSTruncate(svd->ds,svd->nconv,PETSC_TRUE)); |
| 567 |
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|
63 | PetscFunctionReturn(PETSC_SUCCESS); |
| 568 | } | ||
| 569 | |||
| 570 | 2196 | static PetscErrorCode SVDLanczosHSVD(SVD svd,PetscReal *alpha,PetscReal *beta,PetscReal *omega,Mat A,Mat AT,BV V,BV U,PetscInt k,PetscInt *n,PetscBool *breakdown) | |
| 571 | { | ||
| 572 | 2196 | PetscInt i; | |
| 573 | 2196 | Vec u,v,ou=svd->workl[0]; | |
| 574 | 2196 | PetscBool lindep=PETSC_FALSE; | |
| 575 | 2196 | PetscReal norm; | |
| 576 | |||
| 577 |
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2196 | PetscFunctionBegin; |
| 578 |
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26201 | for (i=k;i<*n;i++) { |
| 579 |
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24005 | PetscCall(BVGetColumn(V,i,&v)); |
| 580 |
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24005 | PetscCall(BVGetColumn(U,i,&u)); |
| 581 |
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24005 | PetscCall(MatMult(A,v,u)); |
| 582 |
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24005 | PetscCall(BVRestoreColumn(V,i,&v)); |
| 583 |
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24005 | PetscCall(BVRestoreColumn(U,i,&u)); |
| 584 |
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24005 | PetscCall(BVOrthonormalizeColumn(U,i,PETSC_FALSE,alpha+i,&lindep)); |
| 585 |
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24005 | omega[i] = PetscSign(alpha[i]); |
| 586 |
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24005 | if (PetscUnlikely(lindep)) { |
| 587 | ✗ | *n = i; | |
| 588 | ✗ | break; | |
| 589 | } | ||
| 590 | |||
| 591 |
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24005 | PetscCall(BVGetColumn(V,i+1,&v)); |
| 592 |
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24005 | PetscCall(BVGetColumn(U,i,&u)); |
| 593 |
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24005 | PetscCall(VecPointwiseMult(ou,svd->omega,u)); |
| 594 |
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24005 | PetscCall(MatMult(AT,ou,v)); |
| 595 |
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24005 | PetscCall(BVRestoreColumn(V,i+1,&v)); |
| 596 |
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24005 | PetscCall(BVRestoreColumn(U,i,&u)); |
| 597 |
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24005 | PetscCall(BVOrthonormalizeColumn(V,i+1,PETSC_FALSE,&norm,&lindep)); |
| 598 | 24005 | beta[i] = omega[i]*norm; | |
| 599 |
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24005 | if (PetscUnlikely(lindep)) { |
| 600 | ✗ | *n = i+1; | |
| 601 | ✗ | break; | |
| 602 | } | ||
| 603 | } | ||
| 604 | |||
| 605 |
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2196 | if (breakdown) *breakdown = lindep; |
| 606 |
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|
637 | PetscFunctionReturn(PETSC_SUCCESS); |
| 607 | } | ||
| 608 | |||
| 609 | 97 | static PetscErrorCode SVDSolve_TRLanczos_HSVD(SVD svd) | |
| 610 | { | ||
| 611 | 97 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 612 | 97 | PetscReal *alpha,*beta,*omega; | |
| 613 | 97 | PetscScalar *w,*aux; | |
| 614 | 97 | PetscInt i,k,l,nv,ld,nini; | |
| 615 | 97 | Mat UU,VV,D,A,AT; | |
| 616 | 97 | BV U,V; | |
| 617 | 97 | PetscBool breakdown=PETSC_FALSE; | |
| 618 | 97 | BVOrthogType orthog; | |
| 619 | 97 | Vec vomega; | |
| 620 | |||
| 621 |
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97 | PetscFunctionBegin; |
| 622 | /* undo the effect of swapping in this function */ | ||
| 623 |
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97 | if (svd->swapped) { |
| 624 | 18 | A = svd->AT; | |
| 625 | 18 | AT = svd->A; | |
| 626 | 18 | U = svd->V; | |
| 627 | 18 | V = svd->U; | |
| 628 | 18 | nini = svd->ninil; | |
| 629 | } else { | ||
| 630 | 79 | A = svd->A; | |
| 631 | 79 | AT = svd->AT; | |
| 632 | 79 | U = svd->U; | |
| 633 | 79 | V = svd->V; | |
| 634 | 79 | nini = svd->nini; | |
| 635 | } | ||
| 636 | /* allocate working space */ | ||
| 637 |
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97 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 638 |
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97 | PetscCall(BVGetOrthogonalization(V,&orthog,NULL,NULL,NULL)); |
| 639 |
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|
97 | PetscCall(PetscMalloc1(ld,&w)); |
| 640 |
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97 | PetscCheck(!lanczos->oneside,PetscObjectComm((PetscObject)svd),PETSC_ERR_SUP,"Oneside orthogonalization not supported for HSVD"); |
| 641 | |||
| 642 | /* normalize start vector */ | ||
| 643 |
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97 | if (!nini) { |
| 644 |
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97 | PetscCall(BVSetRandomColumn(V,0)); |
| 645 |
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|
97 | PetscCall(BVOrthonormalizeColumn(V,0,PETSC_TRUE,NULL,NULL)); |
| 646 | } | ||
| 647 | |||
| 648 | l = 0; | ||
| 649 | 2293 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 650 | 2196 | svd->its++; | |
| 651 | |||
| 652 | /* inner loop */ | ||
| 653 | 2196 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 654 |
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2196 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 655 | 2196 | beta = alpha + ld; | |
| 656 |
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2196 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_D,&omega)); |
| 657 |
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|
2196 | PetscCall(SVDLanczosHSVD(svd,alpha,beta,omega,A,AT,V,U,svd->nconv+l,&nv,&breakdown)); |
| 658 |
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2196 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 659 |
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2196 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_D,&omega)); |
| 660 |
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2196 | PetscCall(BVSetActiveColumns(V,svd->nconv,nv)); |
| 661 |
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2196 | PetscCall(BVSetActiveColumns(U,svd->nconv,nv)); |
| 662 | |||
| 663 | /* solve projected problem */ | ||
| 664 |
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2196 | PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,svd->nconv+l)); |
| 665 |
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2196 | PetscCall(DSHSVDSetDimensions(svd->ds,nv)); |
| 666 |
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2196 | PetscCall(DSSetState(svd->ds,l?DS_STATE_RAW:DS_STATE_INTERMEDIATE)); |
| 667 |
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2196 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 668 |
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2196 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 669 |
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2196 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 670 |
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2196 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 671 |
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|
2196 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_D,&omega)); |
| 672 |
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|
47261 | for (i=svd->nconv;i<nv;i++) { |
| 673 | 45065 | svd->sigma[i] = PetscRealPart(w[i]); | |
| 674 | 45065 | svd->sign[i] = omega[i]; | |
| 675 | } | ||
| 676 |
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2196 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_D,&omega)); |
| 677 | |||
| 678 | /* check convergence */ | ||
| 679 |
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|
2196 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,1.0,&k)); |
| 680 |
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2196 | SVDSetCtxThreshold(svd,svd->sigma,k); |
| 681 |
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|
2196 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 682 | |||
| 683 | /* update l */ | ||
| 684 |
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|
2196 | if (svd->reason != SVD_CONVERGED_ITERATING || breakdown || k==nv) l = 0; |
| 685 | 2099 | else l = PetscMax(1,(PetscInt)((nv-k)*lanczos->keep)); | |
| 686 |
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2196 | if (!lanczos->lock && l>0) { l += k; k = 0; } /* non-locking variant: reset no. of converged triplets */ |
| 687 |
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|
2196 | if (l) PetscCall(PetscInfo(svd,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l)); |
| 688 | |||
| 689 |
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2196 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 690 |
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2099 | if (PetscUnlikely(breakdown || k==nv)) { |
| 691 | /* Start a new bidiagonalization */ | ||
| 692 | ✗ | PetscCall(PetscInfo(svd,"Breakdown in bidiagonalization (it=%" PetscInt_FMT ")\n",svd->its)); | |
| 693 | ✗ | if (k<svd->nsv) { | |
| 694 | ✗ | PetscCall(BVSetRandomColumn(V,k)); | |
| 695 | ✗ | PetscCall(BVOrthonormalizeColumn(V,k,PETSC_FALSE,NULL,&breakdown)); | |
| 696 | ✗ | if (breakdown) { | |
| 697 | ✗ | svd->reason = SVD_DIVERGED_BREAKDOWN; | |
| 698 | ✗ | PetscCall(PetscInfo(svd,"Unable to generate more start vectors\n")); | |
| 699 | } | ||
| 700 | } | ||
| 701 |
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|
2099 | } else PetscCall(DSTruncate(svd->ds,k+l,PETSC_FALSE)); |
| 702 | } | ||
| 703 | |||
| 704 | /* compute converged singular vectors and restart vectors */ | ||
| 705 |
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2196 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&VV)); |
| 706 |
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2196 | PetscCall(BVMultInPlace(V,VV,svd->nconv,k+l)); |
| 707 |
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2196 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&VV)); |
| 708 |
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2196 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&UU)); |
| 709 |
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2196 | PetscCall(BVMultInPlace(U,UU,svd->nconv,k+l)); |
| 710 |
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2196 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&UU)); |
| 711 | |||
| 712 |
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2196 | if (svd->reason == SVD_CONVERGED_ITERATING && !breakdown) { |
| 713 | /* copy the last vector of V to be the next initial vector | ||
| 714 | and change signature matrix of U */ | ||
| 715 |
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2099 | PetscCall(BVCopyColumn(V,nv,k+l)); |
| 716 |
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2099 | PetscCall(BVSetActiveColumns(U,0,k+l)); |
| 717 |
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2099 | PetscCall(DSGetMatAndColumn(svd->ds,DS_MAT_D,0,&D,&vomega)); |
| 718 |
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2099 | PetscCall(BVSetSignature(U,vomega)); |
| 719 |
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2099 | PetscCall(DSRestoreMatAndColumn(svd->ds,DS_MAT_D,0,&D,&vomega)); |
| 720 | |||
| 721 |
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|
2099 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 722 | 11 | svd->ncv = svd->mpd+k; | |
| 723 |
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11 | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); |
| 724 |
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81 | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; |
| 725 |
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11 | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); |
| 726 | 11 | aux = w; | |
| 727 |
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11 | PetscCall(PetscMalloc1(svd->ncv+1,&w)); |
| 728 |
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11 | PetscCall(PetscArraycpy(w,aux,ld)); |
| 729 |
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11 | PetscCall(PetscFree(aux)); |
| 730 |
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11 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 731 | } | ||
| 732 | } | ||
| 733 | |||
| 734 | 2196 | svd->nconv = k; | |
| 735 |
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|
2293 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,nv)); |
| 736 | } | ||
| 737 | |||
| 738 | /* free working space */ | ||
| 739 |
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97 | PetscCall(PetscFree(w)); |
| 740 |
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97 | PetscCall(DSTruncate(svd->ds,svd->nconv,PETSC_TRUE)); |
| 741 |
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|
27 | PetscFunctionReturn(PETSC_SUCCESS); |
| 742 | } | ||
| 743 | |||
| 744 | /* Given n computed generalized singular values in sigmain, backtransform them | ||
| 745 | in sigmaout by undoing scaling and reciprocating if swapped=true. Also updates vectors V | ||
| 746 | if given. If sigmaout=NULL then the result overwrites sigmain. */ | ||
| 747 | 6985 | static PetscErrorCode SVDLanczosBackTransform(SVD svd,PetscInt n,PetscReal *sigmain,PetscReal *sigmaout,BV V) | |
| 748 | { | ||
| 749 | 6985 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 750 | 6985 | PetscInt i; | |
| 751 | 6985 | PetscReal c,s,r,f,scalef; | |
| 752 | |||
| 753 |
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6985 | PetscFunctionBegin; |
| 754 |
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6985 | scalef = svd->swapped? 1.0/lanczos->scalef: lanczos->scalef; |
| 755 |
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69932 | for (i=0;i<n;i++) { |
| 756 |
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62947 | if (V && scalef != 1.0) { |
| 757 | 597 | s = 1.0/PetscSqrtReal(1.0+sigmain[i]*sigmain[i]); | |
| 758 | 597 | c = sigmain[i]*s; | |
| 759 | 597 | r = s/scalef; | |
| 760 | 597 | f = 1.0/PetscSqrtReal(c*c+r*r); | |
| 761 |
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597 | PetscCall(BVScaleColumn(V,i,f)); |
| 762 | } | ||
| 763 |
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62947 | if (sigmaout) { |
| 764 |
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|
60804 | if (svd->swapped) sigmaout[i] = 1.0/(sigmain[i]*scalef); |
| 765 | 56282 | else sigmaout[i] = sigmain[i]*scalef; | |
| 766 | } else { | ||
| 767 | 2143 | sigmain[i] *= scalef; | |
| 768 |
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2143 | if (svd->swapped) sigmain[i] = 1.0/sigmain[i]; |
| 769 | } | ||
| 770 | } | ||
| 771 |
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|
1741 | PetscFunctionReturn(PETSC_SUCCESS); |
| 772 | } | ||
| 773 | |||
| 774 | 1638 | static PetscErrorCode SVDLanczosGSingle(SVD svd,PetscReal *alpha,PetscReal *beta,Mat Z,BV V,BV U,KSP ksp,PetscInt k,PetscInt *n,PetscBool *breakdown) | |
| 775 | { | ||
| 776 | 1638 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 777 | 1638 | PetscInt i,j,m; | |
| 778 | 1638 | const PetscScalar *carr; | |
| 779 | 1638 | PetscScalar *arr; | |
| 780 | 1638 | Vec u,v,ut=svd->workl[0],x=svd->workr[0],v1,u1,u2; | |
| 781 | 1638 | PetscBool lindep=PETSC_FALSE; | |
| 782 | |||
| 783 |
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1638 | PetscFunctionBegin; |
| 784 |
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1638 | PetscCall(MatCreateVecsEmpty(svd->A,NULL,&v1)); |
| 785 |
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1638 | PetscCall(BVGetColumn(V,k,&v)); |
| 786 |
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1638 | PetscCall(BVGetColumn(U,k,&u)); |
| 787 | |||
| 788 | /* Form ut=[u;0] */ | ||
| 789 |
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1638 | PetscCall(VecZeroEntries(ut)); |
| 790 |
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1638 | PetscCall(VecGetLocalSize(u,&m)); |
| 791 |
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1638 | PetscCall(VecGetArrayRead(u,&carr)); |
| 792 |
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1638 | PetscCall(VecGetArray(ut,&arr)); |
| 793 |
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|
51084 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 794 |
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1638 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 795 |
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|
1638 | PetscCall(VecRestoreArray(ut,&arr)); |
| 796 | |||
| 797 | /* Solve least squares problem */ | ||
| 798 |
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|
1638 | PetscCall(KSPSolve(ksp,ut,x)); |
| 799 | |||
| 800 |
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|
1638 | PetscCall(MatMult(Z,x,v)); |
| 801 | |||
| 802 |
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|
1638 | PetscCall(BVRestoreColumn(U,k,&u)); |
| 803 |
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|
1638 | PetscCall(BVRestoreColumn(V,k,&v)); |
| 804 |
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|
1638 | PetscCall(BVOrthonormalizeColumn(V,k,PETSC_FALSE,alpha+k,&lindep)); |
| 805 |
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|
1638 | if (PetscUnlikely(lindep)) { |
| 806 | ✗ | *n = k; | |
| 807 | ✗ | if (breakdown) *breakdown = lindep; | |
| 808 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 809 | } | ||
| 810 | |||
| 811 |
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|
8804 | for (i=k+1; i<*n; i++) { |
| 812 | |||
| 813 | /* Compute vector i of BV U */ | ||
| 814 |
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|
7166 | PetscCall(BVGetColumn(V,i-1,&v)); |
| 815 |
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|
7166 | PetscCall(VecGetArray(v,&arr)); |
| 816 |
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|
7166 | PetscCall(VecPlaceArray(v1,arr)); |
| 817 |
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|
7166 | PetscCall(VecRestoreArray(v,&arr)); |
| 818 |
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|
7166 | PetscCall(BVRestoreColumn(V,i-1,&v)); |
| 819 |
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|
7166 | PetscCall(BVInsertVec(U,i,v1)); |
| 820 |
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|
7166 | PetscCall(VecResetArray(v1)); |
| 821 |
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|
7166 | PetscCall(BVOrthonormalizeColumn(U,i,PETSC_FALSE,beta+i-1,&lindep)); |
| 822 |
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|
7166 | if (PetscUnlikely(lindep)) { |
| 823 | ✗ | *n = i; | |
| 824 | ✗ | break; | |
| 825 | } | ||
| 826 | |||
| 827 | /* Compute vector i of BV V */ | ||
| 828 | |||
| 829 |
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|
7166 | PetscCall(BVGetColumn(V,i,&v)); |
| 830 |
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|
7166 | PetscCall(BVGetColumn(U,i,&u)); |
| 831 | |||
| 832 | /* Form ut=[u;0] */ | ||
| 833 |
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|
7166 | PetscCall(VecGetArrayRead(u,&carr)); |
| 834 |
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|
7166 | PetscCall(VecGetArray(ut,&arr)); |
| 835 |
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|
202447 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 836 |
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|
7166 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 837 |
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|
7166 | PetscCall(VecRestoreArray(ut,&arr)); |
| 838 | |||
| 839 | /* Solve least squares problem */ | ||
| 840 |
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|
7166 | PetscCall(KSPSolve(ksp,ut,x)); |
| 841 | |||
| 842 |
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|
7166 | PetscCall(MatMult(Z,x,v)); |
| 843 | |||
| 844 |
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|
7166 | PetscCall(BVRestoreColumn(U,i,&u)); |
| 845 |
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|
7166 | PetscCall(BVRestoreColumn(V,i,&v)); |
| 846 |
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|
7166 | if (!lanczos->oneside || i==k+1) PetscCall(BVOrthonormalizeColumn(V,i,PETSC_FALSE,alpha+i,&lindep)); |
| 847 | else { /* cheap computation of V[i], if restart (i==k+1) do a full reorthogonalization */ | ||
| 848 |
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|
2016 | PetscCall(BVGetColumn(V,i,&u2)); |
| 849 |
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|
2016 | PetscCall(BVGetColumn(V,i-1,&u1)); |
| 850 |
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|
2016 | PetscCall(VecAXPY(u2,-beta[i-1],u1)); |
| 851 |
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|
2016 | PetscCall(BVRestoreColumn(V,i-1,&u1)); |
| 852 |
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|
2016 | PetscCall(VecNorm(u2,NORM_2,&alpha[i])); |
| 853 |
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|
2016 | if (alpha[i]==0.0) lindep = PETSC_TRUE; |
| 854 |
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|
2016 | else PetscCall(VecScale(u2,1.0/alpha[i])); |
| 855 |
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|
2016 | PetscCall(BVRestoreColumn(V,i,&u2)); |
| 856 | } | ||
| 857 |
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|
7166 | if (PetscUnlikely(lindep)) { |
| 858 | ✗ | *n = i; | |
| 859 | ✗ | break; | |
| 860 | } | ||
| 861 | } | ||
| 862 | |||
| 863 | /* Compute vector n of BV U */ | ||
| 864 |
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|
1638 | if (!lindep) { |
| 865 |
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|
1638 | PetscCall(BVGetColumn(V,*n-1,&v)); |
| 866 |
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1638 | PetscCall(VecGetArray(v,&arr)); |
| 867 |
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1638 | PetscCall(VecPlaceArray(v1,arr)); |
| 868 |
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1638 | PetscCall(VecRestoreArray(v,&arr)); |
| 869 |
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|
1638 | PetscCall(BVRestoreColumn(V,*n-1,&v)); |
| 870 |
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|
1638 | PetscCall(BVInsertVec(U,*n,v1)); |
| 871 |
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1638 | PetscCall(VecResetArray(v1)); |
| 872 |
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|
1638 | PetscCall(BVOrthonormalizeColumn(U,*n,PETSC_FALSE,beta+*n-1,&lindep)); |
| 873 | } | ||
| 874 |
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|
1638 | if (breakdown) *breakdown = lindep; |
| 875 |
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|
1638 | PetscCall(VecDestroy(&v1)); |
| 876 |
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|
405 | PetscFunctionReturn(PETSC_SUCCESS); |
| 877 | } | ||
| 878 | |||
| 879 | /* solve generalized problem with single bidiagonalization of Q_A */ | ||
| 880 | 91 | static PetscErrorCode SVDSolve_TRLanczosGSingle(SVD svd,BV U1,BV V) | |
| 881 | { | ||
| 882 | 91 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 883 | 91 | PetscReal *alpha,*beta,normr,scaleth,sigma0,*sigma,*aux2; | |
| 884 | 91 | PetscScalar *w,*aux1; | |
| 885 | 91 | PetscInt i,k,l,nv,ld; | |
| 886 | 91 | Mat U,VV; | |
| 887 | 91 | PetscBool breakdown=PETSC_FALSE; | |
| 888 | |||
| 889 |
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91 | PetscFunctionBegin; |
| 890 |
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91 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 891 |
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|
91 | PetscCall(PetscMalloc2(ld,&w,ld,&sigma)); |
| 892 |
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|
91 | normr = (svd->conv==SVD_CONV_ABS)? PetscMax(svd->nrma,svd->nrmb*lanczos->scalef): 1.0; |
| 893 | /* Convert scale threshold th=c/s to the corresponding c */ | ||
| 894 |
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|
91 | scaleth = (lanczos->scaleth!=0)? lanczos->scaleth/PetscSqrtReal(lanczos->scaleth*lanczos->scaleth+1): 0.0; |
| 895 | |||
| 896 | /* normalize start vector */ | ||
| 897 |
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91 | if (!svd->ninil) { |
| 898 |
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|
83 | PetscCall(BVSetRandomColumn(U1,0)); |
| 899 |
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|
83 | PetscCall(BVOrthonormalizeColumn(U1,0,PETSC_TRUE,NULL,NULL)); |
| 900 | } | ||
| 901 | |||
| 902 | l = 0; | ||
| 903 | 1729 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 904 | 1638 | svd->its++; | |
| 905 | |||
| 906 | /* inner loop */ | ||
| 907 | 1638 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 908 |
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|
1638 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 909 | 1638 | beta = alpha + ld; | |
| 910 |
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|
1638 | PetscCall(SVDLanczosGSingle(svd,alpha,beta,lanczos->Z,V,U1,lanczos->ksp,svd->nconv+l,&nv,&breakdown)); |
| 911 |
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1638 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 912 |
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|
1638 | PetscCall(BVSetActiveColumns(V,svd->nconv,nv)); |
| 913 |
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|
1638 | PetscCall(BVSetActiveColumns(U1,svd->nconv,nv)); |
| 914 | |||
| 915 | /* solve projected problem */ | ||
| 916 |
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1638 | PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,svd->nconv+l)); |
| 917 |
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1638 | PetscCall(DSSVDSetDimensions(svd->ds,nv)); |
| 918 |
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|
1638 | PetscCall(DSSetState(svd->ds,l?DS_STATE_RAW:DS_STATE_INTERMEDIATE)); |
| 919 |
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1638 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 920 |
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1638 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 921 |
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|
1638 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 922 |
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1638 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 923 |
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|
16171 | for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]); |
| 924 | |||
| 925 | /* check convergence */ | ||
| 926 |
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|
1638 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,normr,&k)); |
| 927 |
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1638 | PetscCall(SVDLanczosBackTransform(svd,nv,svd->sigma,sigma,NULL)); |
| 928 |
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|
1638 | SVDSetCtxThreshold(svd,sigma,k); |
| 929 |
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|
1638 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 930 | |||
| 931 |
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|
1638 | sigma0 = svd->which==SVD_LARGEST? svd->sigma[0] : 1.0/svd->sigma[0]; |
| 932 |
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|
1638 | if (scaleth!=0 && k==0 && sigma0>scaleth) { |
| 933 | |||
| 934 | /* Scale and start from scratch */ | ||
| 935 | ✗ | lanczos->scalef *= svd->sigma[0]/PetscSqrtReal(1-svd->sigma[0]*svd->sigma[0]); | |
| 936 | ✗ | PetscCall(PetscInfo(svd,"Scaling by factor %g and starting from scratch\n",(double)lanczos->scalef)); | |
| 937 | ✗ | PetscCall(MatZUpdateScale(svd)); | |
| 938 | ✗ | if (svd->conv==SVD_CONV_ABS) normr = PetscMax(svd->nrma,svd->nrmb*lanczos->scalef); | |
| 939 | l = 0; | ||
| 940 | |||
| 941 | } else { | ||
| 942 | |||
| 943 | /* update l */ | ||
| 944 |
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1638 | if (svd->reason != SVD_CONVERGED_ITERATING || breakdown || k==nv) l = 0; |
| 945 | 1547 | else l = PetscMax(1,(PetscInt)((nv-k)*lanczos->keep)); | |
| 946 |
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|
1638 | if (!lanczos->lock && l>0) { l += k; k = 0; } /* non-locking variant: reset no. of converged triplets */ |
| 947 |
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|
1638 | if (l) PetscCall(PetscInfo(svd,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l)); |
| 948 | |||
| 949 |
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1638 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 950 |
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|
1547 | if (PetscUnlikely(breakdown || k==nv)) { |
| 951 | /* Start a new bidiagonalization */ | ||
| 952 | ✗ | PetscCall(PetscInfo(svd,"Breakdown in bidiagonalization (it=%" PetscInt_FMT ")\n",svd->its)); | |
| 953 | ✗ | if (k<svd->nsv) { | |
| 954 | ✗ | PetscCall(BVSetRandomColumn(U1,k)); | |
| 955 | ✗ | PetscCall(BVOrthonormalizeColumn(U1,k,PETSC_FALSE,NULL,&breakdown)); | |
| 956 | ✗ | if (breakdown) { | |
| 957 | ✗ | svd->reason = SVD_DIVERGED_BREAKDOWN; | |
| 958 | ✗ | PetscCall(PetscInfo(svd,"Unable to generate more start vectors\n")); | |
| 959 | } | ||
| 960 | } | ||
| 961 |
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|
1547 | } else PetscCall(DSTruncate(svd->ds,k+l,PETSC_FALSE)); |
| 962 | } | ||
| 963 | |||
| 964 | /* compute converged singular vectors and restart vectors */ | ||
| 965 |
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|
1638 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U)); |
| 966 |
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|
1638 | PetscCall(BVMultInPlace(V,U,svd->nconv,k+l)); |
| 967 |
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|
1638 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U)); |
| 968 |
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|
1638 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&VV)); |
| 969 |
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1638 | PetscCall(BVMultInPlace(U1,VV,svd->nconv,k+l)); |
| 970 |
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1638 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&VV)); |
| 971 | |||
| 972 |
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1638 | if (svd->reason == SVD_CONVERGED_ITERATING && !breakdown) { |
| 973 |
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|
1547 | PetscCall(BVCopyColumn(U1,nv,k+l)); /* copy the last vector to be the next initial vector */ |
| 974 |
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|
1547 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 975 | 4 | svd->ncv = svd->mpd+k; | |
| 976 |
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|
4 | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); |
| 977 |
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|
4 | PetscCall(BVResize(V,svd->ncv+1,PETSC_TRUE)); |
| 978 |
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4 | PetscCall(BVResize(U1,svd->ncv+1,PETSC_TRUE)); |
| 979 |
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|
28 | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; |
| 980 |
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|
4 | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); |
| 981 | 4 | aux1 = w; | |
| 982 | 4 | aux2 = sigma; | |
| 983 |
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4 | PetscCall(PetscMalloc2(svd->ncv+1,&w,svd->ncv+1,&sigma)); |
| 984 |
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4 | PetscCall(PetscArraycpy(w,aux1,ld)); |
| 985 |
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4 | PetscCall(PetscArraycpy(sigma,aux2,ld)); |
| 986 |
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|
4 | PetscCall(PetscFree2(aux1,aux2)); |
| 987 |
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|
4 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 988 | } | ||
| 989 | } | ||
| 990 | } | ||
| 991 | |||
| 992 | 1638 | svd->nconv = k; | |
| 993 |
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|
1729 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,sigma,svd->errest,nv)); |
| 994 | } | ||
| 995 | |||
| 996 |
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|
91 | PetscCall(PetscFree2(w,sigma)); |
| 997 |
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|
22 | PetscFunctionReturn(PETSC_SUCCESS); |
| 998 | } | ||
| 999 | |||
| 1000 | /* Move generalized left singular vectors (0..nconv) from U1 and U2 to its final destination svd->U (single variant) */ | ||
| 1001 | 91 | static inline PetscErrorCode SVDLeftSingularVectors_Single(SVD svd,BV U1,BV U2) | |
| 1002 | { | ||
| 1003 | 91 | PetscInt i,k,m,p; | |
| 1004 | 91 | Vec u,u1,u2; | |
| 1005 | 91 | PetscScalar *ua,*u2a; | |
| 1006 | 91 | const PetscScalar *u1a; | |
| 1007 | 91 | PetscReal s; | |
| 1008 | |||
| 1009 |
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91 | PetscFunctionBegin; |
| 1010 |
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|
91 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1011 |
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|
91 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 1012 |
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|
495 | for (i=0;i<svd->nconv;i++) { |
| 1013 |
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404 | PetscCall(BVGetColumn(U1,i,&u1)); |
| 1014 |
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404 | PetscCall(BVGetColumn(U2,i,&u2)); |
| 1015 |
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404 | PetscCall(BVGetColumn(svd->U,i,&u)); |
| 1016 |
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404 | PetscCall(VecGetArrayRead(u1,&u1a)); |
| 1017 |
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404 | PetscCall(VecGetArray(u,&ua)); |
| 1018 |
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|
404 | PetscCall(VecGetArray(u2,&u2a)); |
| 1019 | /* Copy column from U1 to upper part of u */ | ||
| 1020 |
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|
18800 | for (k=0;k<m;k++) ua[k] = u1a[k]; |
| 1021 | /* Copy column from lower part of U to U2. Orthogonalize column in U2 and copy back to U */ | ||
| 1022 |
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|
20436 | for (k=0;k<p;k++) u2a[k] = ua[m+k]; |
| 1023 |
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404 | PetscCall(VecRestoreArray(u2,&u2a)); |
| 1024 |
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404 | PetscCall(BVRestoreColumn(U2,i,&u2)); |
| 1025 |
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404 | PetscCall(BVOrthonormalizeColumn(U2,i,PETSC_FALSE,&s,NULL)); |
| 1026 |
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404 | PetscCall(BVGetColumn(U2,i,&u2)); |
| 1027 |
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404 | PetscCall(VecGetArray(u2,&u2a)); |
| 1028 |
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20436 | for (k=0;k<p;k++) ua[m+k] = u2a[k]; |
| 1029 | /* Update singular value */ | ||
| 1030 | 404 | svd->sigma[i] /= s; | |
| 1031 |
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404 | PetscCall(VecRestoreArrayRead(u1,&u1a)); |
| 1032 |
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404 | PetscCall(VecRestoreArray(u,&ua)); |
| 1033 |
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404 | PetscCall(VecRestoreArray(u2,&u2a)); |
| 1034 |
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404 | PetscCall(BVRestoreColumn(U1,i,&u1)); |
| 1035 |
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404 | PetscCall(BVRestoreColumn(U2,i,&u2)); |
| 1036 |
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404 | PetscCall(BVRestoreColumn(svd->U,i,&u)); |
| 1037 | } | ||
| 1038 |
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|
22 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1039 | } | ||
| 1040 | |||
| 1041 | 1986 | static PetscErrorCode SVDLanczosGUpper(SVD svd,PetscReal *alpha,PetscReal *beta,PetscReal *alphah,PetscReal *betah,Mat Z,BV U1,BV U2,BV V,KSP ksp,PetscInt k,PetscInt *n,PetscBool *breakdown) | |
| 1042 | { | ||
| 1043 | 1986 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1044 | 1986 | PetscInt i,j,m,p; | |
| 1045 | 1986 | const PetscScalar *carr; | |
| 1046 | 1986 | PetscScalar *arr,*u2arr; | |
| 1047 | 1986 | Vec u,v,ut=svd->workl[0],x=svd->workr[0],v1,u1,u2; | |
| 1048 | 1986 | PetscBool lindep=PETSC_FALSE,lindep1=PETSC_FALSE,lindep2=PETSC_FALSE; | |
| 1049 | |||
| 1050 |
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1986 | PetscFunctionBegin; |
| 1051 |
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1986 | PetscCall(MatCreateVecsEmpty(svd->A,NULL,&v1)); |
| 1052 |
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1986 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1053 |
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1986 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 1054 | |||
| 1055 |
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|
12359 | for (i=k; i<*n; i++) { |
| 1056 | /* Compute vector i of BV U1 */ | ||
| 1057 |
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10373 | PetscCall(BVGetColumn(V,i,&v)); |
| 1058 |
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10373 | PetscCall(VecGetArrayRead(v,&carr)); |
| 1059 |
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10373 | PetscCall(VecPlaceArray(v1,carr)); |
| 1060 |
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10373 | PetscCall(BVInsertVec(U1,i,v1)); |
| 1061 |
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10373 | PetscCall(VecResetArray(v1)); |
| 1062 |
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|
10373 | if (!lanczos->oneside || i==k) PetscCall(BVOrthonormalizeColumn(U1,i,PETSC_FALSE,alpha+i,&lindep1)); |
| 1063 | else { /* cheap computation of U1[i], if restart (i==k) do a full reorthogonalization */ | ||
| 1064 |
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3450 | PetscCall(BVGetColumn(U1,i,&u2)); |
| 1065 |
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3450 | if (i>0) { |
| 1066 |
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3450 | PetscCall(BVGetColumn(U1,i-1,&u1)); |
| 1067 |
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3450 | PetscCall(VecAXPY(u2,-beta[i-1],u1)); |
| 1068 |
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3450 | PetscCall(BVRestoreColumn(U1,i-1,&u1)); |
| 1069 | } | ||
| 1070 |
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3450 | PetscCall(VecNorm(u2,NORM_2,&alpha[i])); |
| 1071 |
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|
3450 | if (alpha[i]==0.0) lindep = PETSC_TRUE; |
| 1072 |
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|
3450 | else PetscCall(VecScale(u2,1.0/alpha[i])); |
| 1073 |
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3450 | PetscCall(BVRestoreColumn(U1,i,&u2)); |
| 1074 | } | ||
| 1075 | |||
| 1076 | /* Compute vector i of BV U2 */ | ||
| 1077 |
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10373 | PetscCall(BVGetColumn(U2,i,&u2)); |
| 1078 |
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|
10373 | PetscCall(VecGetArray(u2,&u2arr)); |
| 1079 |
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10373 | if (i%2) { |
| 1080 |
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|
239797 | for (j=0; j<p; j++) u2arr[j] = -carr[m+j]; |
| 1081 | } else { | ||
| 1082 |
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232993 | for (j=0; j<p; j++) u2arr[j] = carr[m+j]; |
| 1083 | } | ||
| 1084 |
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10373 | PetscCall(VecRestoreArray(u2,&u2arr)); |
| 1085 |
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10373 | PetscCall(VecRestoreArrayRead(v,&carr)); |
| 1086 |
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|
10373 | PetscCall(BVRestoreColumn(V,i,&v)); |
| 1087 |
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|
10373 | if (lanczos->oneside && i>k) { /* cheap computation of U2[i], if restart (i==k) do a full reorthogonalization */ |
| 1088 |
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3450 | if (i>0) { |
| 1089 |
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3450 | PetscCall(BVGetColumn(U2,i-1,&u1)); |
| 1090 |
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|
3450 | PetscCall(VecAXPY(u2,(i%2)?betah[i-1]:-betah[i-1],u1)); |
| 1091 |
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3450 | PetscCall(BVRestoreColumn(U2,i-1,&u1)); |
| 1092 | } | ||
| 1093 |
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|
3450 | PetscCall(VecNorm(u2,NORM_2,&alphah[i])); |
| 1094 |
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3450 | if (alphah[i]==0.0) lindep = PETSC_TRUE; |
| 1095 |
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|
3450 | else PetscCall(VecScale(u2,1.0/alphah[i])); |
| 1096 | } | ||
| 1097 |
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10373 | PetscCall(BVRestoreColumn(U2,i,&u2)); |
| 1098 |
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|
10373 | if (!lanczos->oneside || i==k) PetscCall(BVOrthonormalizeColumn(U2,i,PETSC_FALSE,alphah+i,&lindep2)); |
| 1099 |
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|
10373 | if (i%2) alphah[i] = -alphah[i]; |
| 1100 |
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|
10373 | if (PetscUnlikely(lindep1 || lindep2)) { |
| 1101 | ✗ | lindep = PETSC_TRUE; | |
| 1102 | ✗ | *n = i; | |
| 1103 | ✗ | break; | |
| 1104 | } | ||
| 1105 | |||
| 1106 | /* Compute vector i+1 of BV V */ | ||
| 1107 |
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10373 | PetscCall(BVGetColumn(V,i+1,&v)); |
| 1108 | /* Form ut=[u;0] */ | ||
| 1109 |
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10373 | PetscCall(BVGetColumn(U1,i,&u)); |
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10373 | PetscCall(VecZeroEntries(ut)); |
| 1111 |
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10373 | PetscCall(VecGetArrayRead(u,&carr)); |
| 1112 |
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10373 | PetscCall(VecGetArray(ut,&arr)); |
| 1113 |
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376633 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 1114 |
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10373 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 1115 |
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|
10373 | PetscCall(VecRestoreArray(ut,&arr)); |
| 1116 | /* Solve least squares problem */ | ||
| 1117 |
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|
10373 | PetscCall(KSPSolve(ksp,ut,x)); |
| 1118 |
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|
10373 | PetscCall(MatMult(Z,x,v)); |
| 1119 |
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|
10373 | PetscCall(BVRestoreColumn(U1,i,&u)); |
| 1120 |
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|
10373 | PetscCall(BVRestoreColumn(V,i+1,&v)); |
| 1121 |
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|
10373 | PetscCall(BVOrthonormalizeColumn(V,i+1,PETSC_FALSE,beta+i,&lindep)); |
| 1122 | 10373 | betah[i] = -alpha[i]*beta[i]/alphah[i]; | |
| 1123 |
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|
10373 | if (PetscUnlikely(lindep)) { |
| 1124 | ✗ | *n = i; | |
| 1125 | ✗ | break; | |
| 1126 | } | ||
| 1127 | } | ||
| 1128 |
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|
1986 | if (breakdown) *breakdown = lindep; |
| 1129 |
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|
1986 | PetscCall(VecDestroy(&v1)); |
| 1130 |
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|
498 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1131 | } | ||
| 1132 | |||
| 1133 | /* generate random initial vector in column k for joint upper-upper bidiagonalization */ | ||
| 1134 | 155 | static inline PetscErrorCode SVDInitialVectorGUpper(SVD svd,BV V,BV U1,PetscInt k,PetscBool *breakdown) | |
| 1135 | { | ||
| 1136 | 155 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1137 | 155 | Vec u,v,ut=svd->workl[0],x=svd->workr[0]; | |
| 1138 | 155 | PetscInt m,j; | |
| 1139 | 155 | PetscScalar *arr; | |
| 1140 | 155 | const PetscScalar *carr; | |
| 1141 | |||
| 1142 |
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155 | PetscFunctionBegin; |
| 1143 | /* Form ut=[u;0] where u is the k-th column of U1 */ | ||
| 1144 |
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155 | PetscCall(VecZeroEntries(ut)); |
| 1145 |
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155 | PetscCall(BVGetColumn(U1,k,&u)); |
| 1146 |
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155 | PetscCall(VecGetLocalSize(u,&m)); |
| 1147 |
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155 | PetscCall(VecGetArrayRead(u,&carr)); |
| 1148 |
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155 | PetscCall(VecGetArray(ut,&arr)); |
| 1149 |
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9407 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 1150 |
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155 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 1151 |
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155 | PetscCall(VecRestoreArray(ut,&arr)); |
| 1152 |
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155 | PetscCall(BVRestoreColumn(U1,k,&u)); |
| 1153 | /* Solve least squares problem Z*x=ut for x. Then set v=Z*x */ | ||
| 1154 |
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155 | PetscCall(KSPSolve(lanczos->ksp,ut,x)); |
| 1155 |
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155 | PetscCall(BVGetColumn(V,k,&v)); |
| 1156 |
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155 | PetscCall(MatMult(lanczos->Z,x,v)); |
| 1157 |
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155 | PetscCall(BVRestoreColumn(V,k,&v)); |
| 1158 |
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155 | if (breakdown) PetscCall(BVOrthonormalizeColumn(V,k,PETSC_FALSE,NULL,breakdown)); |
| 1159 |
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155 | else PetscCall(BVOrthonormalizeColumn(V,k,PETSC_TRUE,NULL,NULL)); |
| 1160 |
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|
39 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1161 | } | ||
| 1162 | |||
| 1163 | /* solve generalized problem with joint upper-upper bidiagonalization */ | ||
| 1164 | 127 | static PetscErrorCode SVDSolve_TRLanczosGUpper(SVD svd,BV U1,BV U2,BV V) | |
| 1165 | { | ||
| 1166 | 127 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1167 | 127 | PetscReal *alpha,*beta,*alphah,*betah,normr,sigma0,*sigma,*aux2; | |
| 1168 | 127 | PetscScalar *w,*aux1; | |
| 1169 | 127 | PetscInt i,k,l,nv,ld; | |
| 1170 | 127 | Mat U,Vmat,X; | |
| 1171 | 127 | PetscBool breakdown=PETSC_FALSE; | |
| 1172 | |||
| 1173 |
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127 | PetscFunctionBegin; |
| 1174 |
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127 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 1175 |
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127 | PetscCall(PetscMalloc2(ld,&w,ld,&sigma)); |
| 1176 |
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127 | normr = (svd->conv==SVD_CONV_ABS)? PetscMax(svd->nrma,svd->nrmb*lanczos->scalef): 1.0; |
| 1177 | |||
| 1178 | /* normalize start vector */ | ||
| 1179 |
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127 | if (!svd->ninil) PetscCall(BVSetRandomColumn(U1,0)); |
| 1180 |
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|
127 | PetscCall(SVDInitialVectorGUpper(svd,V,U1,0,NULL)); |
| 1181 | |||
| 1182 | l = 0; | ||
| 1183 | 2113 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 1184 | 1986 | svd->its++; | |
| 1185 | |||
| 1186 | /* inner loop */ | ||
| 1187 | 1986 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 1188 |
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1986 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1189 |
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|
1986 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_D,&alphah)); |
| 1190 | 1986 | beta = alpha + ld; | |
| 1191 | 1986 | betah = alpha + 2*ld; | |
| 1192 |
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|
1986 | PetscCall(SVDLanczosGUpper(svd,alpha,beta,alphah,betah,lanczos->Z,U1,U2,V,lanczos->ksp,svd->nconv+l,&nv,&breakdown)); |
| 1193 |
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1986 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1194 |
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1986 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_D,&alphah)); |
| 1195 |
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1986 | PetscCall(BVSetActiveColumns(V,svd->nconv,nv)); |
| 1196 |
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1986 | PetscCall(BVSetActiveColumns(U1,svd->nconv,nv)); |
| 1197 |
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|
1986 | PetscCall(BVSetActiveColumns(U2,svd->nconv,nv)); |
| 1198 | |||
| 1199 | /* solve projected problem */ | ||
| 1200 |
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1986 | PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,svd->nconv+l)); |
| 1201 |
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1986 | PetscCall(DSGSVDSetDimensions(svd->ds,nv,nv)); |
| 1202 |
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1986 | PetscCall(DSSetState(svd->ds,l?DS_STATE_RAW:DS_STATE_INTERMEDIATE)); |
| 1203 |
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1986 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 1204 |
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|
1986 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 1205 |
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1986 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 1206 |
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1986 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 1207 |
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|
18801 | for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]); |
| 1208 | |||
| 1209 | /* check convergence */ | ||
| 1210 |
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1986 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,normr,&k)); |
| 1211 |
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1986 | PetscCall(SVDLanczosBackTransform(svd,nv,svd->sigma,sigma,NULL)); |
| 1212 |
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|
1986 | SVDSetCtxThreshold(svd,sigma,k); |
| 1213 |
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|
1986 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 1214 | |||
| 1215 |
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|
1986 | sigma0 = svd->which==SVD_LARGEST? svd->sigma[0] : 1.0/svd->sigma[0]; |
| 1216 |
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|
1986 | if (lanczos->scaleth!=0 && k==0 && sigma0>lanczos->scaleth) { |
| 1217 | |||
| 1218 | /* Scale and start from scratch */ | ||
| 1219 | 28 | lanczos->scalef *= svd->sigma[0]; | |
| 1220 |
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|
28 | PetscCall(PetscInfo(svd,"Scaling by factor %g and starting from scratch\n",(double)lanczos->scalef)); |
| 1221 |
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|
28 | PetscCall(MatZUpdateScale(svd)); |
| 1222 |
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|
28 | if (svd->conv==SVD_CONV_ABS) normr = PetscMax(svd->nrma,svd->nrmb*lanczos->scalef); |
| 1223 | 28 | l = 0; | |
| 1224 |
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|
28 | if (!svd->ninil) PetscCall(BVSetRandomColumn(U1,0)); |
| 1225 |
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|
28 | PetscCall(SVDInitialVectorGUpper(svd,V,U1,0,NULL)); |
| 1226 | |||
| 1227 | } else { | ||
| 1228 | |||
| 1229 | /* update l */ | ||
| 1230 |
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|
1958 | if (svd->reason != SVD_CONVERGED_ITERATING || breakdown || k==nv) l = 0; |
| 1231 | 1831 | else l = PetscMax(1,(PetscInt)((nv-k)*lanczos->keep)); | |
| 1232 |
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|
1958 | if (!lanczos->lock && l>0) { l += k; k = 0; } /* non-locking variant: reset no. of converged triplets */ |
| 1233 |
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|
1958 | if (l) PetscCall(PetscInfo(svd,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l)); |
| 1234 | |||
| 1235 |
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1958 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 1236 |
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|
1831 | if (PetscUnlikely(breakdown || k==nv)) { |
| 1237 | /* Start a new bidiagonalization */ | ||
| 1238 | ✗ | PetscCall(PetscInfo(svd,"Breakdown in bidiagonalization (it=%" PetscInt_FMT ")\n",svd->its)); | |
| 1239 | ✗ | if (k<svd->nsv) { | |
| 1240 | ✗ | PetscCall(BVSetRandomColumn(U1,k)); | |
| 1241 | ✗ | PetscCall(SVDInitialVectorGUpper(svd,V,U1,k,&breakdown)); | |
| 1242 | ✗ | if (breakdown) { | |
| 1243 | ✗ | svd->reason = SVD_DIVERGED_BREAKDOWN; | |
| 1244 | ✗ | PetscCall(PetscInfo(svd,"Unable to generate more start vectors\n")); | |
| 1245 | } | ||
| 1246 | } | ||
| 1247 |
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|
1831 | } else PetscCall(DSTruncate(svd->ds,k+l,PETSC_FALSE)); |
| 1248 | } | ||
| 1249 | /* compute converged singular vectors and restart vectors */ | ||
| 1250 |
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|
1958 | PetscCall(DSGetMat(svd->ds,DS_MAT_X,&X)); |
| 1251 |
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|
1958 | PetscCall(BVMultInPlace(V,X,svd->nconv,k+l)); |
| 1252 |
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|
1958 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_X,&X)); |
| 1253 |
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|
1958 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U)); |
| 1254 |
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|
1958 | PetscCall(BVMultInPlace(U1,U,svd->nconv,k+l)); |
| 1255 |
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|
1958 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U)); |
| 1256 |
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|
1958 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&Vmat)); |
| 1257 |
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|
1958 | PetscCall(BVMultInPlace(U2,Vmat,svd->nconv,k+l)); |
| 1258 |
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|
1958 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&Vmat)); |
| 1259 | |||
| 1260 |
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1958 | if (svd->reason == SVD_CONVERGED_ITERATING && !breakdown) { |
| 1261 |
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|
1831 | PetscCall(BVCopyColumn(V,nv,k+l)); /* copy the last vector to be the next initial vector */ |
| 1262 |
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|
1831 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 1263 | 4 | svd->ncv = svd->mpd+k; | |
| 1264 |
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|
4 | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); |
| 1265 |
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|
4 | PetscCall(BVResize(U1,svd->ncv+1,PETSC_TRUE)); |
| 1266 |
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4 | PetscCall(BVResize(U2,svd->ncv+1,PETSC_TRUE)); |
| 1267 |
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28 | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; |
| 1268 |
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|
4 | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); |
| 1269 | 4 | aux1 = w; | |
| 1270 | 4 | aux2 = sigma; | |
| 1271 |
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4 | PetscCall(PetscMalloc2(svd->ncv+1,&w,svd->ncv+1,&sigma)); |
| 1272 |
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4 | PetscCall(PetscArraycpy(w,aux1,ld)); |
| 1273 |
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4 | PetscCall(PetscArraycpy(sigma,aux2,ld)); |
| 1274 |
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4 | PetscCall(PetscFree2(aux1,aux2)); |
| 1275 |
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|
4 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 1276 | } | ||
| 1277 | } | ||
| 1278 | } | ||
| 1279 | |||
| 1280 | 1986 | svd->nconv = k; | |
| 1281 |
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|
2113 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,sigma,svd->errest,nv)); |
| 1282 | } | ||
| 1283 | |||
| 1284 |
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127 | PetscCall(PetscFree2(w,sigma)); |
| 1285 |
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|
32 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1286 | } | ||
| 1287 | |||
| 1288 | /* Move generalized left singular vectors (0..nconv) from U1 and U2 to its final destination svd->U (upper and lower variants) */ | ||
| 1289 | 406 | static inline PetscErrorCode SVDLeftSingularVectors(SVD svd,BV U1,BV U2) | |
| 1290 | { | ||
| 1291 | 406 | PetscInt i,k,m,p; | |
| 1292 | 406 | Vec u,u1,u2; | |
| 1293 | 406 | PetscScalar *ua; | |
| 1294 | 406 | const PetscScalar *u1a,*u2a; | |
| 1295 | |||
| 1296 |
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406 | PetscFunctionBegin; |
| 1297 |
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406 | PetscCall(BVGetSizes(U1,&m,NULL,NULL)); |
| 1298 |
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406 | PetscCall(BVGetSizes(U2,&p,NULL,NULL)); |
| 1299 |
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2145 | for (i=0;i<svd->nconv;i++) { |
| 1300 |
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1739 | PetscCall(BVGetColumn(U1,i,&u1)); |
| 1301 |
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1739 | PetscCall(BVGetColumn(U2,i,&u2)); |
| 1302 |
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1739 | PetscCall(BVGetColumn(svd->U,i,&u)); |
| 1303 |
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1739 | PetscCall(VecGetArrayRead(u1,&u1a)); |
| 1304 |
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1739 | PetscCall(VecGetArrayRead(u2,&u2a)); |
| 1305 |
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1739 | PetscCall(VecGetArray(u,&ua)); |
| 1306 | /* Copy column from u1 to upper part of u */ | ||
| 1307 |
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109781 | for (k=0;k<m;k++) ua[k] = u1a[k]; |
| 1308 | /* Copy column from u2 to lower part of u */ | ||
| 1309 |
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151019 | for (k=0;k<p;k++) ua[m+k] = u2a[k]; |
| 1310 |
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1739 | PetscCall(VecRestoreArrayRead(u1,&u1a)); |
| 1311 |
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1739 | PetscCall(VecRestoreArrayRead(u2,&u2a)); |
| 1312 |
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1739 | PetscCall(VecRestoreArray(u,&ua)); |
| 1313 |
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1739 | PetscCall(BVRestoreColumn(U1,i,&u1)); |
| 1314 |
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1739 | PetscCall(BVRestoreColumn(U2,i,&u2)); |
| 1315 |
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1739 | PetscCall(BVRestoreColumn(svd->U,i,&u)); |
| 1316 | } | ||
| 1317 |
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|
104 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1318 | } | ||
| 1319 | |||
| 1320 | 2864 | static PetscErrorCode SVDLanczosGLower(SVD svd,PetscReal *alpha,PetscReal *beta,PetscReal *alphah,PetscReal *betah,Mat Z,BV U1,BV U2,BV V,KSP ksp,PetscInt k,PetscInt *n,PetscBool *breakdown) | |
| 1321 | { | ||
| 1322 | 2864 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1323 | 2864 | PetscInt i,j,m,p; | |
| 1324 | 2864 | const PetscScalar *carr; | |
| 1325 | 2864 | PetscScalar *arr,*u2arr; | |
| 1326 | 2864 | Vec u,v,ut=svd->workl[0],x=svd->workr[0],v1,u1,u2; | |
| 1327 | 2864 | PetscBool lindep=PETSC_FALSE; | |
| 1328 | |||
| 1329 |
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2864 | PetscFunctionBegin; |
| 1330 |
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2864 | PetscCall(MatCreateVecsEmpty(svd->A,NULL,&v1)); |
| 1331 |
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2864 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1332 |
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2864 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 1333 | |||
| 1334 |
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18905 | for (i=k; i<*n; i++) { |
| 1335 | /* Compute vector i of BV U2 */ | ||
| 1336 |
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16041 | PetscCall(BVGetColumn(V,i,&v)); |
| 1337 |
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16041 | PetscCall(VecGetArrayRead(v,&carr)); |
| 1338 |
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16041 | PetscCall(BVGetColumn(U2,i,&u2)); |
| 1339 |
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16041 | PetscCall(VecGetArray(u2,&u2arr)); |
| 1340 |
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16041 | if (i%2) { |
| 1341 |
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423995 | for (j=0; j<p; j++) u2arr[j] = -carr[m+j]; |
| 1342 | } else { | ||
| 1343 |
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396683 | for (j=0; j<p; j++) u2arr[j] = carr[m+j]; |
| 1344 | } | ||
| 1345 |
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16041 | PetscCall(VecRestoreArray(u2,&u2arr)); |
| 1346 |
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|
16041 | if (lanczos->oneside && i>k) { /* cheap computation of U2[i], if restart (i==k) do a full reorthogonalization */ |
| 1347 |
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3360 | if (i>0) { |
| 1348 |
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3360 | PetscCall(BVGetColumn(U2,i-1,&u1)); |
| 1349 |
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3360 | PetscCall(VecAXPY(u2,(i%2)?betah[i-1]:-betah[i-1],u1)); |
| 1350 |
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|
3360 | PetscCall(BVRestoreColumn(U2,i-1,&u1)); |
| 1351 | } | ||
| 1352 |
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|
3360 | PetscCall(VecNorm(u2,NORM_2,&alphah[i])); |
| 1353 |
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|
3360 | if (alphah[i]==0.0) lindep = PETSC_TRUE; |
| 1354 |
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|
3360 | else PetscCall(VecScale(u2,1.0/alphah[i])); |
| 1355 | } | ||
| 1356 |
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|
16041 | PetscCall(BVRestoreColumn(U2,i,&u2)); |
| 1357 |
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|
16041 | if (!lanczos->oneside || i==k) PetscCall(BVOrthonormalizeColumn(U2,i,PETSC_FALSE,alphah+i,&lindep)); |
| 1358 |
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|
16041 | if (i%2) alphah[i] = -alphah[i]; |
| 1359 |
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|
16041 | if (PetscUnlikely(lindep)) { |
| 1360 | ✗ | PetscCall(BVRestoreColumn(V,i,&v)); | |
| 1361 | ✗ | *n = i; | |
| 1362 | ✗ | break; | |
| 1363 | } | ||
| 1364 | |||
| 1365 | /* Compute vector i+1 of BV U1 */ | ||
| 1366 |
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16041 | PetscCall(VecPlaceArray(v1,carr)); |
| 1367 |
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16041 | PetscCall(BVInsertVec(U1,i+1,v1)); |
| 1368 |
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16041 | PetscCall(VecResetArray(v1)); |
| 1369 |
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|
16041 | PetscCall(BVOrthonormalizeColumn(U1,i+1,PETSC_FALSE,beta+i,&lindep)); |
| 1370 |
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16041 | PetscCall(VecRestoreArrayRead(v,&carr)); |
| 1371 |
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|
16041 | PetscCall(BVRestoreColumn(V,i,&v)); |
| 1372 |
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16041 | if (PetscUnlikely(lindep)) { |
| 1373 | ✗ | *n = i+1; | |
| 1374 | ✗ | break; | |
| 1375 | } | ||
| 1376 | |||
| 1377 | /* Compute vector i+1 of BV V */ | ||
| 1378 |
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|
16041 | PetscCall(BVGetColumn(V,i+1,&v)); |
| 1379 | /* Form ut=[u;0] where u is column i+1 of BV U1 */ | ||
| 1380 |
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16041 | PetscCall(BVGetColumn(U1,i+1,&u)); |
| 1381 |
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16041 | PetscCall(VecZeroEntries(ut)); |
| 1382 |
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16041 | PetscCall(VecGetArrayRead(u,&carr)); |
| 1383 |
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16041 | PetscCall(VecGetArray(ut,&arr)); |
| 1384 |
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642353 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 1385 |
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16041 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 1386 |
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|
16041 | PetscCall(VecRestoreArray(ut,&arr)); |
| 1387 | /* Solve least squares problem */ | ||
| 1388 |
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|
16041 | PetscCall(KSPSolve(ksp,ut,x)); |
| 1389 |
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|
16041 | PetscCall(MatMult(Z,x,v)); |
| 1390 |
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|
16041 | PetscCall(BVRestoreColumn(U1,i+1,&u)); |
| 1391 |
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|
16041 | PetscCall(BVRestoreColumn(V,i+1,&v)); |
| 1392 |
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|
16041 | if (!lanczos->oneside || i==k) PetscCall(BVOrthonormalizeColumn(V,i+1,PETSC_FALSE,alpha+i+1,&lindep)); |
| 1393 | else { /* cheap computation of V[i+1], if restart (i==k) do a full reorthogonalization */ | ||
| 1394 |
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|
3360 | PetscCall(BVGetColumn(V,i+1,&u2)); |
| 1395 |
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3360 | PetscCall(BVGetColumn(V,i,&u1)); |
| 1396 |
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|
3360 | PetscCall(VecAXPY(u2,-beta[i],u1)); |
| 1397 |
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3360 | PetscCall(BVRestoreColumn(V,i,&u1)); |
| 1398 |
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|
3360 | PetscCall(VecNorm(u2,NORM_2,&alpha[i+1])); |
| 1399 |
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3360 | if (alpha[i+1]==0.0) lindep = PETSC_TRUE; |
| 1400 |
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|
3360 | else PetscCall(VecScale(u2,1.0/alpha[i+1])); |
| 1401 |
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|
3360 | PetscCall(BVRestoreColumn(V,i+1,&u2)); |
| 1402 | } | ||
| 1403 | 16041 | betah[i] = -alpha[i+1]*beta[i]/alphah[i]; | |
| 1404 |
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|
16041 | if (PetscUnlikely(lindep)) { |
| 1405 | ✗ | *n = i+1; | |
| 1406 | ✗ | break; | |
| 1407 | } | ||
| 1408 | } | ||
| 1409 |
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|
2864 | if (breakdown) *breakdown = lindep; |
| 1410 |
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|
2864 | PetscCall(VecDestroy(&v1)); |
| 1411 |
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|
712 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1412 | } | ||
| 1413 | |||
| 1414 | /* generate random initial vector in column k for joint lower-upper bidiagonalization */ | ||
| 1415 | 335 | static inline PetscErrorCode SVDInitialVectorGLower(SVD svd,BV V,BV U1,BV U2,PetscInt k,PetscBool *breakdown) | |
| 1416 | { | ||
| 1417 | 335 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1418 | 335 | const PetscScalar *carr; | |
| 1419 | 335 | PetscScalar *arr; | |
| 1420 | 335 | PetscReal *alpha; | |
| 1421 | 335 | PetscInt j,m,p; | |
| 1422 | 335 | Vec u,uh,v,ut=svd->workl[0],x=svd->workr[0]; | |
| 1423 | |||
| 1424 |
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335 | PetscFunctionBegin; |
| 1425 |
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335 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1426 |
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|
335 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 1427 | /* Form ut=[0;uh], where uh is the k-th column of U2 */ | ||
| 1428 |
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335 | PetscCall(BVGetColumn(U2,k,&uh)); |
| 1429 |
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335 | PetscCall(VecZeroEntries(ut)); |
| 1430 |
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335 | PetscCall(VecGetArrayRead(uh,&carr)); |
| 1431 |
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335 | PetscCall(VecGetArray(ut,&arr)); |
| 1432 |
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28864 | for (j=0; j<p; j++) arr[m+j] = carr[j]; |
| 1433 |
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335 | PetscCall(VecRestoreArrayRead(uh,&carr)); |
| 1434 |
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335 | PetscCall(VecRestoreArray(ut,&arr)); |
| 1435 |
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|
335 | PetscCall(BVRestoreColumn(U2,k,&uh)); |
| 1436 | /* Solve least squares problem Z*x=ut for x. Then set ut=Z*x */ | ||
| 1437 |
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335 | PetscCall(KSPSolve(lanczos->ksp,ut,x)); |
| 1438 |
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|
335 | PetscCall(MatMult(lanczos->Z,x,ut)); |
| 1439 | /* Form u, column k of BV U1, as the upper part of ut and orthonormalize */ | ||
| 1440 |
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335 | PetscCall(MatCreateVecsEmpty(svd->A,NULL,&u)); |
| 1441 |
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335 | PetscCall(VecGetArrayRead(ut,&carr)); |
| 1442 |
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335 | PetscCall(VecPlaceArray(u,carr)); |
| 1443 |
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335 | PetscCall(BVInsertVec(U1,k,u)); |
| 1444 |
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335 | PetscCall(VecResetArray(u)); |
| 1445 |
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335 | PetscCall(VecRestoreArrayRead(ut,&carr)); |
| 1446 |
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335 | PetscCall(VecDestroy(&u)); |
| 1447 |
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|
335 | if (breakdown) PetscCall(BVOrthonormalizeColumn(U1,k,PETSC_FALSE,NULL,breakdown)); |
| 1448 |
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|
335 | else PetscCall(BVOrthonormalizeColumn(U1,k,PETSC_TRUE,NULL,NULL)); |
| 1449 | |||
| 1450 |
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335 | if (!breakdown || !*breakdown) { |
| 1451 |
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335 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1452 | /* Compute k-th vector of BV V */ | ||
| 1453 |
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335 | PetscCall(BVGetColumn(V,k,&v)); |
| 1454 | /* Form ut=[u;0] where u is the 1st column of U1 */ | ||
| 1455 |
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335 | PetscCall(BVGetColumn(U1,k,&u)); |
| 1456 |
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335 | PetscCall(VecZeroEntries(ut)); |
| 1457 |
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335 | PetscCall(VecGetArrayRead(u,&carr)); |
| 1458 |
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335 | PetscCall(VecGetArray(ut,&arr)); |
| 1459 |
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20099 | for (j=0; j<m; j++) arr[j] = carr[j]; |
| 1460 |
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335 | PetscCall(VecRestoreArrayRead(u,&carr)); |
| 1461 |
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335 | PetscCall(VecRestoreArray(ut,&arr)); |
| 1462 | /* Solve least squares problem */ | ||
| 1463 |
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335 | PetscCall(KSPSolve(lanczos->ksp,ut,x)); |
| 1464 |
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335 | PetscCall(MatMult(lanczos->Z,x,v)); |
| 1465 |
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335 | PetscCall(BVRestoreColumn(U1,k,&u)); |
| 1466 |
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335 | PetscCall(BVRestoreColumn(V,k,&v)); |
| 1467 |
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335 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1468 |
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335 | if (breakdown) PetscCall(BVOrthonormalizeColumn(V,k,PETSC_FALSE,alpha+k,breakdown)); |
| 1469 |
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|
335 | else PetscCall(BVOrthonormalizeColumn(V,k,PETSC_TRUE,alpha+k,NULL)); |
| 1470 |
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335 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1471 | } | ||
| 1472 |
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|
86 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1473 | } | ||
| 1474 | |||
| 1475 | /* solve generalized problem with joint lower-upper bidiagonalization */ | ||
| 1476 | 279 | static PetscErrorCode SVDSolve_TRLanczosGLower(SVD svd,BV U1,BV U2,BV V) | |
| 1477 | { | ||
| 1478 | 279 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1479 | 279 | PetscReal *alpha,*beta,*alphah,*betah,normr,scalef,*sigma,sigma0,*aux2; | |
| 1480 | 279 | PetscScalar *w,*aux1; | |
| 1481 | 279 | PetscInt i,k,l,nv,ld; | |
| 1482 | 279 | Mat U,Vmat,X; | |
| 1483 | 279 | PetscBool breakdown=PETSC_FALSE,inverted; | |
| 1484 | |||
| 1485 |
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279 | PetscFunctionBegin; |
| 1486 |
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279 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 1487 |
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279 | PetscCall(PetscMalloc2(ld,&w,ld,&sigma)); |
| 1488 |
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|
279 | inverted = ((svd->which==SVD_LARGEST && svd->swapped) || (svd->which==SVD_SMALLEST && !svd->swapped))? PETSC_TRUE: PETSC_FALSE; |
| 1489 |
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279 | scalef = svd->swapped? 1.0/lanczos->scalef : lanczos->scalef; |
| 1490 |
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279 | normr = (svd->conv==SVD_CONV_ABS)? PetscMax(svd->nrma,svd->nrmb*scalef): 1.0; |
| 1491 | |||
| 1492 | /* normalize start vector */ | ||
| 1493 |
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279 | if (!svd->ninil) PetscCall(BVSetRandomColumn(U2,0)); |
| 1494 |
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|
279 | PetscCall(SVDInitialVectorGLower(svd,V,U1,U2,0,NULL)); |
| 1495 | |||
| 1496 | l = 0; | ||
| 1497 | 3143 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 1498 | 2864 | svd->its++; | |
| 1499 | |||
| 1500 | /* inner loop */ | ||
| 1501 | 2864 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 1502 |
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2864 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1503 |
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|
2864 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_D,&alphah)); |
| 1504 | 2864 | beta = alpha + ld; | |
| 1505 | 2864 | betah = alpha + 2*ld; | |
| 1506 |
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|
2864 | PetscCall(SVDLanczosGLower(svd,alpha,beta,alphah,betah,lanczos->Z,U1,U2,V,lanczos->ksp,svd->nconv+l,&nv,&breakdown)); |
| 1507 |
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2864 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 1508 |
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2864 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_D,&alphah)); |
| 1509 |
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2864 | PetscCall(BVSetActiveColumns(V,svd->nconv,nv)); |
| 1510 |
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2864 | PetscCall(BVSetActiveColumns(U1,svd->nconv,nv+1)); |
| 1511 |
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2864 | PetscCall(BVSetActiveColumns(U2,svd->nconv,nv)); |
| 1512 | |||
| 1513 | /* solve projected problem */ | ||
| 1514 |
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2864 | PetscCall(DSSetDimensions(svd->ds,nv+1,svd->nconv,svd->nconv+l)); |
| 1515 |
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2864 | PetscCall(DSGSVDSetDimensions(svd->ds,nv,nv)); |
| 1516 |
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2864 | PetscCall(DSSetState(svd->ds,l?DS_STATE_RAW:DS_STATE_INTERMEDIATE)); |
| 1517 |
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2864 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 1518 |
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2864 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 1519 |
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2864 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 1520 |
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2864 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 1521 |
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28851 | for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]); |
| 1522 | |||
| 1523 | /* check convergence */ | ||
| 1524 |
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2864 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,normr,&k)); |
| 1525 |
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2864 | PetscCall(SVDLanczosBackTransform(svd,nv,svd->sigma,sigma,NULL)); |
| 1526 |
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2864 | SVDSetCtxThreshold(svd,sigma,k); |
| 1527 |
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2864 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 1528 | |||
| 1529 |
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2864 | sigma0 = inverted? 1.0/svd->sigma[0] : svd->sigma[0]; |
| 1530 |
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2864 | if (lanczos->scaleth!=0 && k==0 && sigma0>lanczos->scaleth) { |
| 1531 | |||
| 1532 | /* Scale and start from scratch */ | ||
| 1533 |
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56 | lanczos->scalef *= svd->swapped? 1.0/svd->sigma[0] : svd->sigma[0]; |
| 1534 |
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56 | PetscCall(PetscInfo(svd,"Scaling by factor %g and starting from scratch\n",(double)lanczos->scalef)); |
| 1535 |
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56 | PetscCall(MatZUpdateScale(svd)); |
| 1536 |
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56 | scalef = svd->swapped? 1.0/lanczos->scalef : lanczos->scalef; |
| 1537 |
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56 | if (svd->conv==SVD_CONV_ABS) normr = PetscMax(svd->nrma,svd->nrmb*scalef); |
| 1538 | 56 | l = 0; | |
| 1539 |
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56 | if (!svd->ninil) PetscCall(BVSetRandomColumn(U2,0)); |
| 1540 |
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56 | PetscCall(SVDInitialVectorGLower(svd,V,U1,U2,0,NULL)); |
| 1541 | |||
| 1542 | } else { | ||
| 1543 | |||
| 1544 | /* update l */ | ||
| 1545 |
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2808 | if (svd->reason != SVD_CONVERGED_ITERATING || breakdown || k==nv) l = 0; |
| 1546 | 2529 | else l = PetscMax(1,(PetscInt)((nv-k)*lanczos->keep)); | |
| 1547 |
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2808 | if (!lanczos->lock && l>0) { l += k; k = 0; } /* non-locking variant: reset no. of converged triplets */ |
| 1548 |
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2808 | if (l) PetscCall(PetscInfo(svd,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l)); |
| 1549 | |||
| 1550 |
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2808 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 1551 |
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2529 | if (PetscUnlikely(breakdown || k==nv)) { |
| 1552 | /* Start a new bidiagonalization */ | ||
| 1553 | ✗ | PetscCall(PetscInfo(svd,"Breakdown in bidiagonalization (it=%" PetscInt_FMT ")\n",svd->its)); | |
| 1554 | ✗ | if (k<svd->nsv) { | |
| 1555 | ✗ | PetscCall(BVSetRandomColumn(U2,k)); | |
| 1556 | ✗ | PetscCall(SVDInitialVectorGLower(svd,V,U1,U2,k,&breakdown)); | |
| 1557 | ✗ | if (breakdown) { | |
| 1558 | ✗ | svd->reason = SVD_DIVERGED_BREAKDOWN; | |
| 1559 | ✗ | PetscCall(PetscInfo(svd,"Unable to generate more start vectors\n")); | |
| 1560 | } | ||
| 1561 | } | ||
| 1562 |
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2529 | } else PetscCall(DSTruncate(svd->ds,k+l,PETSC_FALSE)); |
| 1563 | } | ||
| 1564 | |||
| 1565 | /* compute converged singular vectors and restart vectors */ | ||
| 1566 |
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2808 | PetscCall(DSGetMat(svd->ds,DS_MAT_X,&X)); |
| 1567 |
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2808 | PetscCall(BVMultInPlace(V,X,svd->nconv,k+l)); |
| 1568 |
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2808 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_X,&X)); |
| 1569 |
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2808 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U)); |
| 1570 |
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2808 | PetscCall(BVMultInPlace(U1,U,svd->nconv,k+l+1)); |
| 1571 |
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2808 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U)); |
| 1572 |
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2808 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&Vmat)); |
| 1573 |
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2808 | PetscCall(BVMultInPlace(U2,Vmat,svd->nconv,k+l)); |
| 1574 |
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2808 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&Vmat)); |
| 1575 | |||
| 1576 |
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2808 | if (svd->reason == SVD_CONVERGED_ITERATING && !breakdown) { |
| 1577 |
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2529 | PetscCall(BVCopyColumn(V,nv,k+l)); /* copy the last vector to be the next initial vector */ |
| 1578 |
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2529 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 1579 | 4 | svd->ncv = svd->mpd+k; | |
| 1580 |
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4 | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); |
| 1581 |
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4 | PetscCall(BVResize(U1,svd->ncv+1,PETSC_TRUE)); |
| 1582 |
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4 | PetscCall(BVResize(U2,svd->ncv+1,PETSC_TRUE)); |
| 1583 |
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28 | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; |
| 1584 |
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4 | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); |
| 1585 | 4 | aux1 = w; | |
| 1586 | 4 | aux2 = sigma; | |
| 1587 |
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4 | PetscCall(PetscMalloc2(svd->ncv+1,&w,svd->ncv+1,&sigma)); |
| 1588 |
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4 | PetscCall(PetscArraycpy(w,aux1,ld)); |
| 1589 |
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4 | PetscCall(PetscArraycpy(sigma,aux2,ld)); |
| 1590 |
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4 | PetscCall(PetscFree2(aux1,aux2)); |
| 1591 |
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4 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 1592 | } | ||
| 1593 | } | ||
| 1594 | } | ||
| 1595 | |||
| 1596 | 2864 | svd->nconv = k; | |
| 1597 |
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|
3143 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,sigma,svd->errest,nv)); |
| 1598 | } | ||
| 1599 | |||
| 1600 |
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279 | PetscCall(PetscFree2(w,sigma)); |
| 1601 |
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|
72 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1602 | } | ||
| 1603 | |||
| 1604 | 497 | static PetscErrorCode SVDSolve_TRLanczos_GSVD(SVD svd) | |
| 1605 | { | ||
| 1606 | 497 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1607 | 497 | PetscInt k,m,p; | |
| 1608 | 497 | PetscBool convchg=PETSC_FALSE; | |
| 1609 | 497 | BV U1,U2,UU; | |
| 1610 | 497 | BVType type; | |
| 1611 | 497 | VecType vtype; | |
| 1612 | 497 | Mat U,V; | |
| 1613 | 497 | SlepcSC sc; | |
| 1614 | |||
| 1615 |
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497 | PetscFunctionBegin; |
| 1616 |
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497 | PetscCall(PetscCitationsRegister(citationg,&citedg)); |
| 1617 | |||
| 1618 |
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497 | if (svd->swapped) { |
| 1619 |
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|
33 | PetscCall(DSGetSlepcSC(svd->ds,&sc)); |
| 1620 |
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33 | if (svd->which==SVD_LARGEST) sc->comparison = SlepcCompareSmallestReal; |
| 1621 | ✗ | else sc->comparison = SlepcCompareLargestReal; | |
| 1622 | } | ||
| 1623 |
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|
497 | if (svd->converged==SVDConvergedNorm) { /* override temporarily since computed residual is already relative to the norms */ |
| 1624 | 497 | svd->converged = SVDConvergedAbsolute; | |
| 1625 | 497 | convchg = PETSC_TRUE; | |
| 1626 | } | ||
| 1627 |
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497 | PetscCall(MatGetLocalSize(svd->A,&m,NULL)); |
| 1628 |
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|
497 | PetscCall(MatGetLocalSize(svd->B,&p,NULL)); |
| 1629 | |||
| 1630 | /* Create BV for U1 */ | ||
| 1631 |
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|
497 | PetscCall(BVCreate(PetscObjectComm((PetscObject)svd),&U1)); |
| 1632 |
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|
497 | PetscCall(BVGetType(svd->U,&type)); |
| 1633 |
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|
497 | PetscCall(BVSetType(U1,type)); |
| 1634 |
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497 | PetscCall(BVGetSizes(svd->U,NULL,NULL,&k)); |
| 1635 |
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|
497 | PetscCall(BVSetSizes(U1,m,PETSC_DECIDE,k)); |
| 1636 |
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497 | PetscCall(BVGetVecType(svd->U,&vtype)); |
| 1637 |
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|
497 | PetscCall(BVSetVecType(U1,vtype)); |
| 1638 | |||
| 1639 | /* Create BV for U2 */ | ||
| 1640 |
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|
497 | PetscCall(BVCreate(PetscObjectComm((PetscObject)svd),&U2)); |
| 1641 |
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|
497 | PetscCall(BVSetType(U2,type)); |
| 1642 |
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|
497 | PetscCall(BVSetSizes(U2,p,PETSC_DECIDE,k)); |
| 1643 |
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|
497 | PetscCall(BVSetVecType(U2,vtype)); |
| 1644 | |||
| 1645 | /* Copy initial vectors from svd->U to U1 and U2 */ | ||
| 1646 |
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|
497 | if (svd->ninil) { |
| 1647 | 32 | Vec u, uh, nest, aux[2]; | |
| 1648 |
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|
32 | PetscCall(BVGetColumn(U1,0,&u)); |
| 1649 |
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|
32 | PetscCall(BVGetColumn(U2,0,&uh)); |
| 1650 | 32 | aux[0] = u; | |
| 1651 | 32 | aux[1] = uh; | |
| 1652 |
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|
32 | PetscCall(VecCreateNest(PetscObjectComm((PetscObject)svd),2,NULL,aux,&nest)); |
| 1653 |
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|
32 | PetscCall(BVCopyVec(svd->U,0,nest)); |
| 1654 |
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|
32 | PetscCall(BVRestoreColumn(U1,0,&u)); |
| 1655 |
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|
32 | PetscCall(BVRestoreColumn(U2,0,&uh)); |
| 1656 |
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|
32 | PetscCall(VecDestroy(&nest)); |
| 1657 | } | ||
| 1658 | |||
| 1659 |
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|
497 | switch (lanczos->bidiag) { |
| 1660 | 91 | case SVD_TRLANCZOS_GBIDIAG_SINGLE: | |
| 1661 |
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|
91 | PetscCall(SVDSolve_TRLanczosGSingle(svd,U1,svd->U)); |
| 1662 |
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|
91 | if (svd->stop==SVD_STOP_THRESHOLD) PetscCall(BVResize(U2,svd->ncv+1,PETSC_FALSE)); |
| 1663 | break; | ||
| 1664 | 127 | case SVD_TRLANCZOS_GBIDIAG_UPPER: | |
| 1665 |
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|
127 | PetscCall(SVDSolve_TRLanczosGUpper(svd,U1,U2,svd->U)); |
| 1666 | break; | ||
| 1667 | 279 | case SVD_TRLANCZOS_GBIDIAG_LOWER: | |
| 1668 |
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|
279 | PetscCall(SVDSolve_TRLanczosGLower(svd,U1,U2,svd->U)); |
| 1669 | break; | ||
| 1670 | } | ||
| 1671 | |||
| 1672 | /* Compute converged right singular vectors */ | ||
| 1673 |
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|
497 | PetscCall(BVSetActiveColumns(svd->U,0,svd->nconv)); |
| 1674 |
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|
497 | PetscCall(BVSetActiveColumns(svd->V,0,svd->nconv)); |
| 1675 |
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|
497 | PetscCall(BVGetMat(svd->U,&U)); |
| 1676 |
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|
497 | PetscCall(BVGetMat(svd->V,&V)); |
| 1677 |
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|
497 | PetscCall(KSPMatSolve(lanczos->ksp,U,V)); |
| 1678 |
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|
497 | PetscCall(BVRestoreMat(svd->U,&U)); |
| 1679 |
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|
497 | PetscCall(BVRestoreMat(svd->V,&V)); |
| 1680 | |||
| 1681 | /* Finish computing left singular vectors and move them to its place */ | ||
| 1682 |
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|
497 | if (svd->swapped) SlepcSwap(U1,U2,UU); |
| 1683 |
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497 | switch (lanczos->bidiag) { |
| 1684 | 91 | case SVD_TRLANCZOS_GBIDIAG_SINGLE: | |
| 1685 |
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|
91 | PetscCall(SVDLeftSingularVectors_Single(svd,U1,U2)); |
| 1686 | break; | ||
| 1687 | 406 | case SVD_TRLANCZOS_GBIDIAG_UPPER: | |
| 1688 | case SVD_TRLANCZOS_GBIDIAG_LOWER: | ||
| 1689 |
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|
406 | PetscCall(SVDLeftSingularVectors(svd,U1,U2)); |
| 1690 | break; | ||
| 1691 | } | ||
| 1692 | |||
| 1693 | /* undo scaling and compute the reciprocals of sigma if matrices were swapped */ | ||
| 1694 |
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|
497 | PetscCall(SVDLanczosBackTransform(svd,svd->nconv,svd->sigma,NULL,svd->V)); |
| 1695 | |||
| 1696 |
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|
497 | PetscCall(BVDestroy(&U1)); |
| 1697 |
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|
497 | PetscCall(BVDestroy(&U2)); |
| 1698 |
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|
497 | PetscCall(DSTruncate(svd->ds,svd->nconv,PETSC_TRUE)); |
| 1699 |
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|
497 | if (convchg) svd->converged = SVDConvergedNorm; |
| 1700 |
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|
126 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1701 | } | ||
| 1702 | |||
| 1703 | 756 | static PetscErrorCode SVDSetFromOptions_TRLanczos(SVD svd,PetscOptionItems PetscOptionsObject) | |
| 1704 | { | ||
| 1705 | 756 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1706 | 756 | PetscBool flg,val,lock; | |
| 1707 | 756 | PetscReal keep,scale; | |
| 1708 | 756 | SVDTRLanczosGBidiag bidiag; | |
| 1709 | |||
| 1710 |
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756 | PetscFunctionBegin; |
| 1711 |
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|
756 | PetscOptionsHeadBegin(PetscOptionsObject,"SVD TRLanczos Options"); |
| 1712 | |||
| 1713 |
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|
756 | PetscCall(PetscOptionsBool("-svd_trlanczos_oneside","Use one-side reorthogonalization","SVDTRLanczosSetOneSide",lanczos->oneside,&val,&flg)); |
| 1714 |
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|
756 | if (flg) PetscCall(SVDTRLanczosSetOneSide(svd,val)); |
| 1715 | |||
| 1716 |
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|
756 | PetscCall(PetscOptionsReal("-svd_trlanczos_restart","Proportion of vectors kept after restart","SVDTRLanczosSetRestart",0.5,&keep,&flg)); |
| 1717 |
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|
756 | if (flg) PetscCall(SVDTRLanczosSetRestart(svd,keep)); |
| 1718 | |||
| 1719 |
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|
756 | PetscCall(PetscOptionsBool("-svd_trlanczos_locking","Choose between locking and non-locking variants","SVDTRLanczosSetLocking",PETSC_TRUE,&lock,&flg)); |
| 1720 |
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|
756 | if (flg) PetscCall(SVDTRLanczosSetLocking(svd,lock)); |
| 1721 | |||
| 1722 |
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|
756 | PetscCall(PetscOptionsEnum("-svd_trlanczos_gbidiag","Bidiagonalization choice for Generalized Problem","SVDTRLanczosSetGBidiag",SVDTRLanczosGBidiags,(PetscEnum)lanczos->bidiag,(PetscEnum*)&bidiag,&flg)); |
| 1723 |
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|
756 | if (flg) PetscCall(SVDTRLanczosSetGBidiag(svd,bidiag)); |
| 1724 | |||
| 1725 |
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|
756 | PetscCall(PetscOptionsBool("-svd_trlanczos_explicitmatrix","Build explicit matrix for KSP solver","SVDTRLanczosSetExplicitMatrix",lanczos->explicitmatrix,&val,&flg)); |
| 1726 |
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756 | if (flg) PetscCall(SVDTRLanczosSetExplicitMatrix(svd,val)); |
| 1727 | |||
| 1728 |
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|
756 | PetscCall(SVDTRLanczosGetScale(svd,&scale)); |
| 1729 |
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756 | PetscCall(PetscOptionsReal("-svd_trlanczos_scale","Scale parameter for matrix B","SVDTRLanczosSetScale",scale,&scale,&flg)); |
| 1730 |
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|
756 | if (flg) PetscCall(SVDTRLanczosSetScale(svd,scale)); |
| 1731 | |||
| 1732 |
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|
756 | PetscOptionsHeadEnd(); |
| 1733 | |||
| 1734 |
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756 | if (svd->OPb) { |
| 1735 |
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|
445 | if (!lanczos->ksp) PetscCall(SVDTRLanczosGetKSP(svd,&lanczos->ksp)); |
| 1736 |
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445 | PetscCall(KSPSetFromOptions(lanczos->ksp)); |
| 1737 | } | ||
| 1738 |
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|
190 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1739 | } | ||
| 1740 | |||
| 1741 | 264 | static PetscErrorCode SVDTRLanczosSetOneSide_TRLanczos(SVD svd,PetscBool oneside) | |
| 1742 | { | ||
| 1743 | 264 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1744 | |||
| 1745 |
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264 | PetscFunctionBegin; |
| 1746 |
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|
264 | if (lanczos->oneside != oneside) { |
| 1747 | 167 | lanczos->oneside = oneside; | |
| 1748 | 167 | svd->state = SVD_STATE_INITIAL; | |
| 1749 | } | ||
| 1750 |
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|
264 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1751 | } | ||
| 1752 | |||
| 1753 | /*@ | ||
| 1754 | SVDTRLanczosSetOneSide - Indicate if the variant of the Lanczos method | ||
| 1755 | to be used is one-sided or two-sided. | ||
| 1756 | |||
| 1757 | Logically Collective | ||
| 1758 | |||
| 1759 | Input Parameters: | ||
| 1760 | + svd - the singular value solver context | ||
| 1761 | - oneside - boolean flag indicating if the method is one-sided or not | ||
| 1762 | |||
| 1763 | Options Database Key: | ||
| 1764 | . -svd_trlanczos_oneside - enable the one-sided variant | ||
| 1765 | |||
| 1766 | Notes: | ||
| 1767 | By default, a two-sided variant is selected, which is sometimes slightly | ||
| 1768 | more robust. However, the one-sided variant is faster because it avoids | ||
| 1769 | the orthogonalization associated to left singular vectors. See more | ||
| 1770 | details in {cite:p}`Her07c,Her08`. | ||
| 1771 | |||
| 1772 | One-sided orthogonalization is also available for the GSVD, in which case | ||
| 1773 | two orthogonalizations out of three are avoided, see {cite:p}`Alv24`. | ||
| 1774 | |||
| 1775 | Level: advanced | ||
| 1776 | |||
| 1777 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDLanczosSetOneSide()` | ||
| 1778 | @*/ | ||
| 1779 | 264 | PetscErrorCode SVDTRLanczosSetOneSide(SVD svd,PetscBool oneside) | |
| 1780 | { | ||
| 1781 |
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264 | PetscFunctionBegin; |
| 1782 |
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264 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1783 |
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264 | PetscValidLogicalCollectiveBool(svd,oneside,2); |
| 1784 |
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264 | PetscTryMethod(svd,"SVDTRLanczosSetOneSide_C",(SVD,PetscBool),(svd,oneside)); |
| 1785 |
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|
264 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1786 | } | ||
| 1787 | |||
| 1788 | 56 | static PetscErrorCode SVDTRLanczosGetOneSide_TRLanczos(SVD svd,PetscBool *oneside) | |
| 1789 | { | ||
| 1790 | 56 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1791 | |||
| 1792 |
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56 | PetscFunctionBegin; |
| 1793 | 56 | *oneside = lanczos->oneside; | |
| 1794 |
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56 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1795 | } | ||
| 1796 | |||
| 1797 | /*@ | ||
| 1798 | SVDTRLanczosGetOneSide - Gets if the variant of the Lanczos method | ||
| 1799 | to be used is one-sided or two-sided. | ||
| 1800 | |||
| 1801 | Not Collective | ||
| 1802 | |||
| 1803 | Input Parameter: | ||
| 1804 | . svd - the singular value solver context | ||
| 1805 | |||
| 1806 | Output Parameter: | ||
| 1807 | . oneside - boolean flag indicating if the method is one-sided or not | ||
| 1808 | |||
| 1809 | Level: advanced | ||
| 1810 | |||
| 1811 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetOneSide()` | ||
| 1812 | @*/ | ||
| 1813 | 56 | PetscErrorCode SVDTRLanczosGetOneSide(SVD svd,PetscBool *oneside) | |
| 1814 | { | ||
| 1815 |
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56 | PetscFunctionBegin; |
| 1816 |
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56 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1817 |
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56 | PetscAssertPointer(oneside,2); |
| 1818 |
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56 | PetscUseMethod(svd,"SVDTRLanczosGetOneSide_C",(SVD,PetscBool*),(svd,oneside)); |
| 1819 |
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56 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1820 | } | ||
| 1821 | |||
| 1822 | 393 | static PetscErrorCode SVDTRLanczosSetGBidiag_TRLanczos(SVD svd,SVDTRLanczosGBidiag bidiag) | |
| 1823 | { | ||
| 1824 | 393 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1825 | |||
| 1826 |
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393 | PetscFunctionBegin; |
| 1827 |
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393 | switch (bidiag) { |
| 1828 | 393 | case SVD_TRLANCZOS_GBIDIAG_SINGLE: | |
| 1829 | case SVD_TRLANCZOS_GBIDIAG_UPPER: | ||
| 1830 | case SVD_TRLANCZOS_GBIDIAG_LOWER: | ||
| 1831 |
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393 | if (lanczos->bidiag != bidiag) { |
| 1832 | 264 | lanczos->bidiag = bidiag; | |
| 1833 | 264 | svd->state = SVD_STATE_INITIAL; | |
| 1834 | } | ||
| 1835 | 98 | break; | |
| 1836 | ✗ | default: | |
| 1837 | ✗ | SETERRQ(PetscObjectComm((PetscObject)svd),PETSC_ERR_ARG_OUTOFRANGE,"Invalid bidiagonalization choice"); | |
| 1838 | } | ||
| 1839 |
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98 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1840 | } | ||
| 1841 | |||
| 1842 | /*@ | ||
| 1843 | SVDTRLanczosSetGBidiag - Sets the bidiagonalization choice to use in | ||
| 1844 | the GSVD `SVDTRLANCZOS` solver. | ||
| 1845 | |||
| 1846 | Logically Collective | ||
| 1847 | |||
| 1848 | Input Parameters: | ||
| 1849 | + svd - the singular value solver context | ||
| 1850 | - bidiag - the bidiagonalization choice, see `SVDTRLanczosGBidiag` for possible values | ||
| 1851 | |||
| 1852 | Options Database Key: | ||
| 1853 | . -svd_trlanczos_gbidiag \<bidiag\> - sets the bidiagonalization choice, either `single`, `upper` or `lower` | ||
| 1854 | |||
| 1855 | Level: advanced | ||
| 1856 | |||
| 1857 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetGBidiag()`, `SVDTRLanczosGBidiag` | ||
| 1858 | @*/ | ||
| 1859 | 393 | PetscErrorCode SVDTRLanczosSetGBidiag(SVD svd,SVDTRLanczosGBidiag bidiag) | |
| 1860 | { | ||
| 1861 |
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393 | PetscFunctionBegin; |
| 1862 |
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393 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1863 |
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393 | PetscValidLogicalCollectiveEnum(svd,bidiag,2); |
| 1864 |
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393 | PetscTryMethod(svd,"SVDTRLanczosSetGBidiag_C",(SVD,SVDTRLanczosGBidiag),(svd,bidiag)); |
| 1865 |
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393 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1866 | } | ||
| 1867 | |||
| 1868 | 48 | static PetscErrorCode SVDTRLanczosGetGBidiag_TRLanczos(SVD svd,SVDTRLanczosGBidiag *bidiag) | |
| 1869 | { | ||
| 1870 | 48 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 1871 | |||
| 1872 |
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48 | PetscFunctionBegin; |
| 1873 | 48 | *bidiag = lanczos->bidiag; | |
| 1874 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1875 | } | ||
| 1876 | |||
| 1877 | /*@ | ||
| 1878 | SVDTRLanczosGetGBidiag - Gets the bidiagonalization choice used in the GSVD | ||
| 1879 | TRLanczos solver. | ||
| 1880 | |||
| 1881 | Not Collective | ||
| 1882 | |||
| 1883 | Input Parameter: | ||
| 1884 | . svd - the singular value solver context | ||
| 1885 | |||
| 1886 | Output Parameter: | ||
| 1887 | . bidiag - the bidiagonalization choice | ||
| 1888 | |||
| 1889 | Level: advanced | ||
| 1890 | |||
| 1891 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetGBidiag()`, `SVDTRLanczosGBidiag` | ||
| 1892 | @*/ | ||
| 1893 | 48 | PetscErrorCode SVDTRLanczosGetGBidiag(SVD svd,SVDTRLanczosGBidiag *bidiag) | |
| 1894 | { | ||
| 1895 |
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48 | PetscFunctionBegin; |
| 1896 |
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48 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1897 |
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48 | PetscAssertPointer(bidiag,2); |
| 1898 |
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48 | PetscUseMethod(svd,"SVDTRLanczosGetGBidiag_C",(SVD,SVDTRLanczosGBidiag*),(svd,bidiag)); |
| 1899 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1900 | } | ||
| 1901 | |||
| 1902 | 48 | static PetscErrorCode SVDTRLanczosSetKSP_TRLanczos(SVD svd,KSP ksp) | |
| 1903 | { | ||
| 1904 | 48 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 1905 | |||
| 1906 |
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48 | PetscFunctionBegin; |
| 1907 |
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48 | PetscCall(PetscObjectReference((PetscObject)ksp)); |
| 1908 |
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48 | PetscCall(KSPDestroy(&ctx->ksp)); |
| 1909 | 48 | ctx->ksp = ksp; | |
| 1910 | 48 | svd->state = SVD_STATE_INITIAL; | |
| 1911 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1912 | } | ||
| 1913 | |||
| 1914 | /*@ | ||
| 1915 | SVDTRLanczosSetKSP - Associate a linear solver object (`KSP`) to the SVD solver. | ||
| 1916 | |||
| 1917 | Collective | ||
| 1918 | |||
| 1919 | Input Parameters: | ||
| 1920 | + svd - the singular value solver context | ||
| 1921 | - ksp - the linear solver object | ||
| 1922 | |||
| 1923 | Note: | ||
| 1924 | Only used for the GSVD problem. | ||
| 1925 | |||
| 1926 | Level: advanced | ||
| 1927 | |||
| 1928 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetKSP()` | ||
| 1929 | @*/ | ||
| 1930 | 48 | PetscErrorCode SVDTRLanczosSetKSP(SVD svd,KSP ksp) | |
| 1931 | { | ||
| 1932 |
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48 | PetscFunctionBegin; |
| 1933 |
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48 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1934 |
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48 | PetscValidHeaderSpecific(ksp,KSP_CLASSID,2); |
| 1935 |
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48 | PetscCheckSameComm(svd,1,ksp,2); |
| 1936 |
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48 | PetscTryMethod(svd,"SVDTRLanczosSetKSP_C",(SVD,KSP),(svd,ksp)); |
| 1937 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1938 | } | ||
| 1939 | |||
| 1940 | 397 | static PetscErrorCode SVDTRLanczosGetKSP_TRLanczos(SVD svd,KSP *ksp) | |
| 1941 | { | ||
| 1942 | 397 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 1943 | 397 | PC pc; | |
| 1944 | |||
| 1945 |
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397 | PetscFunctionBegin; |
| 1946 |
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397 | if (!ctx->ksp) { |
| 1947 | /* Create linear solver */ | ||
| 1948 |
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397 | PetscCall(KSPCreate(PetscObjectComm((PetscObject)svd),&ctx->ksp)); |
| 1949 |
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397 | PetscCall(PetscObjectIncrementTabLevel((PetscObject)ctx->ksp,(PetscObject)svd,1)); |
| 1950 |
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397 | PetscCall(KSPSetOptionsPrefix(ctx->ksp,((PetscObject)svd)->prefix)); |
| 1951 |
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397 | PetscCall(KSPAppendOptionsPrefix(ctx->ksp,"svd_trlanczos_")); |
| 1952 |
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397 | PetscCall(PetscObjectSetOptions((PetscObject)ctx->ksp,((PetscObject)svd)->options)); |
| 1953 |
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397 | PetscCall(KSPSetType(ctx->ksp,KSPLSQR)); |
| 1954 |
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397 | PetscCall(KSPGetPC(ctx->ksp,&pc)); |
| 1955 |
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397 | PetscCall(PCSetType(pc,PCNONE)); |
| 1956 |
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397 | PetscCall(KSPSetErrorIfNotConverged(ctx->ksp,PETSC_TRUE)); |
| 1957 |
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|
785 | PetscCall(KSPSetTolerances(ctx->ksp,SlepcDefaultTol(svd->tol)/10.0,PETSC_CURRENT,PETSC_CURRENT,PETSC_CURRENT)); |
| 1958 | } | ||
| 1959 | 397 | *ksp = ctx->ksp; | |
| 1960 |
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397 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1961 | } | ||
| 1962 | |||
| 1963 | /*@ | ||
| 1964 | SVDTRLanczosGetKSP - Retrieve the linear solver object (`KSP`) associated with | ||
| 1965 | the `SVDTRLANCZOS` solver. | ||
| 1966 | |||
| 1967 | Collective | ||
| 1968 | |||
| 1969 | Input Parameter: | ||
| 1970 | . svd - the singular value solver context | ||
| 1971 | |||
| 1972 | Output Parameter: | ||
| 1973 | . ksp - the linear solver object | ||
| 1974 | |||
| 1975 | Level: advanced | ||
| 1976 | |||
| 1977 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetKSP()` | ||
| 1978 | @*/ | ||
| 1979 | 397 | PetscErrorCode SVDTRLanczosGetKSP(SVD svd,KSP *ksp) | |
| 1980 | { | ||
| 1981 |
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397 | PetscFunctionBegin; |
| 1982 |
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397 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 1983 |
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397 | PetscAssertPointer(ksp,2); |
| 1984 |
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397 | PetscUseMethod(svd,"SVDTRLanczosGetKSP_C",(SVD,KSP*),(svd,ksp)); |
| 1985 |
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397 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1986 | } | ||
| 1987 | |||
| 1988 | 112 | static PetscErrorCode SVDTRLanczosSetRestart_TRLanczos(SVD svd,PetscReal keep) | |
| 1989 | { | ||
| 1990 | 112 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 1991 | |||
| 1992 |
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112 | PetscFunctionBegin; |
| 1993 |
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112 | if (keep==(PetscReal)PETSC_DEFAULT || keep==(PetscReal)PETSC_DECIDE) ctx->keep = 0.5; |
| 1994 | else { | ||
| 1995 |
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112 | PetscCheck(keep>=0.1 && keep<=0.9,PetscObjectComm((PetscObject)svd),PETSC_ERR_ARG_OUTOFRANGE,"The keep argument %g must be in the range [0.1,0.9]",(double)keep); |
| 1996 | 112 | ctx->keep = keep; | |
| 1997 | } | ||
| 1998 |
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28 | PetscFunctionReturn(PETSC_SUCCESS); |
| 1999 | } | ||
| 2000 | |||
| 2001 | /*@ | ||
| 2002 | SVDTRLanczosSetRestart - Sets the restart parameter for the thick-restart | ||
| 2003 | Lanczos method, in particular the proportion of basis vectors that must be | ||
| 2004 | kept after restart. | ||
| 2005 | |||
| 2006 | Logically Collective | ||
| 2007 | |||
| 2008 | Input Parameters: | ||
| 2009 | + svd - the singular value solver context | ||
| 2010 | - keep - the number of vectors to be kept at restart | ||
| 2011 | |||
| 2012 | Options Database Key: | ||
| 2013 | . -svd_trlanczos_restart \<keep\> - sets the restart parameter | ||
| 2014 | |||
| 2015 | Notes: | ||
| 2016 | Allowed values are in the range [0.1,0.9]. The default is 0.5, which means | ||
| 2017 | that at restart the current subspace is compressed into another subspace | ||
| 2018 | with a reduction of 50% in size. | ||
| 2019 | |||
| 2020 | Level: advanced | ||
| 2021 | |||
| 2022 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetRestart()` | ||
| 2023 | @*/ | ||
| 2024 | 112 | PetscErrorCode SVDTRLanczosSetRestart(SVD svd,PetscReal keep) | |
| 2025 | { | ||
| 2026 |
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112 | PetscFunctionBegin; |
| 2027 |
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112 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2028 |
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|
112 | PetscValidLogicalCollectiveReal(svd,keep,2); |
| 2029 |
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112 | PetscTryMethod(svd,"SVDTRLanczosSetRestart_C",(SVD,PetscReal),(svd,keep)); |
| 2030 |
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112 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2031 | } | ||
| 2032 | |||
| 2033 | 48 | static PetscErrorCode SVDTRLanczosGetRestart_TRLanczos(SVD svd,PetscReal *keep) | |
| 2034 | { | ||
| 2035 | 48 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 2036 | |||
| 2037 |
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48 | PetscFunctionBegin; |
| 2038 | 48 | *keep = ctx->keep; | |
| 2039 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2040 | } | ||
| 2041 | |||
| 2042 | /*@ | ||
| 2043 | SVDTRLanczosGetRestart - Gets the restart parameter used in the thick-restart | ||
| 2044 | Lanczos method. | ||
| 2045 | |||
| 2046 | Not Collective | ||
| 2047 | |||
| 2048 | Input Parameter: | ||
| 2049 | . svd - the singular value solver context | ||
| 2050 | |||
| 2051 | Output Parameter: | ||
| 2052 | . keep - the restart parameter | ||
| 2053 | |||
| 2054 | Level: advanced | ||
| 2055 | |||
| 2056 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetRestart()` | ||
| 2057 | @*/ | ||
| 2058 | 48 | PetscErrorCode SVDTRLanczosGetRestart(SVD svd,PetscReal *keep) | |
| 2059 | { | ||
| 2060 |
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48 | PetscFunctionBegin; |
| 2061 |
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48 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2062 |
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48 | PetscAssertPointer(keep,2); |
| 2063 |
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48 | PetscUseMethod(svd,"SVDTRLanczosGetRestart_C",(SVD,PetscReal*),(svd,keep)); |
| 2064 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2065 | } | ||
| 2066 | |||
| 2067 | 160 | static PetscErrorCode SVDTRLanczosSetLocking_TRLanczos(SVD svd,PetscBool lock) | |
| 2068 | { | ||
| 2069 | 160 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 2070 | |||
| 2071 |
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160 | PetscFunctionBegin; |
| 2072 | 160 | ctx->lock = lock; | |
| 2073 |
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160 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2074 | } | ||
| 2075 | |||
| 2076 | /*@ | ||
| 2077 | SVDTRLanczosSetLocking - Choose between locking and non-locking variants of | ||
| 2078 | the thick-restart Lanczos method. | ||
| 2079 | |||
| 2080 | Logically Collective | ||
| 2081 | |||
| 2082 | Input Parameters: | ||
| 2083 | + svd - the singular value solver context | ||
| 2084 | - lock - `PETSC_TRUE` if the locking variant must be selected | ||
| 2085 | |||
| 2086 | Options Database Key: | ||
| 2087 | . -svd_trlanczos_locking - sets the locking flag | ||
| 2088 | |||
| 2089 | Notes: | ||
| 2090 | The default is to lock converged singular triplets when the method restarts. | ||
| 2091 | This behavior can be changed so that all directions are kept in the | ||
| 2092 | working subspace even if already converged to working accuracy (the | ||
| 2093 | non-locking variant). | ||
| 2094 | |||
| 2095 | Level: advanced | ||
| 2096 | |||
| 2097 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetLocking()` | ||
| 2098 | @*/ | ||
| 2099 | 160 | PetscErrorCode SVDTRLanczosSetLocking(SVD svd,PetscBool lock) | |
| 2100 | { | ||
| 2101 |
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160 | PetscFunctionBegin; |
| 2102 |
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160 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2103 |
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160 | PetscValidLogicalCollectiveBool(svd,lock,2); |
| 2104 |
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160 | PetscTryMethod(svd,"SVDTRLanczosSetLocking_C",(SVD,PetscBool),(svd,lock)); |
| 2105 |
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160 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2106 | } | ||
| 2107 | |||
| 2108 | 48 | static PetscErrorCode SVDTRLanczosGetLocking_TRLanczos(SVD svd,PetscBool *lock) | |
| 2109 | { | ||
| 2110 | 48 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 2111 | |||
| 2112 |
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48 | PetscFunctionBegin; |
| 2113 | 48 | *lock = ctx->lock; | |
| 2114 |
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48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2115 | } | ||
| 2116 | |||
| 2117 | /*@ | ||
| 2118 | SVDTRLanczosGetLocking - Gets the locking flag used in the thick-restart | ||
| 2119 | Lanczos method. | ||
| 2120 | |||
| 2121 | Not Collective | ||
| 2122 | |||
| 2123 | Input Parameter: | ||
| 2124 | . svd - the singular value solver context | ||
| 2125 | |||
| 2126 | Output Parameter: | ||
| 2127 | . lock - the locking flag | ||
| 2128 | |||
| 2129 | Level: advanced | ||
| 2130 | |||
| 2131 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetLocking()` | ||
| 2132 | @*/ | ||
| 2133 | 48 | PetscErrorCode SVDTRLanczosGetLocking(SVD svd,PetscBool *lock) | |
| 2134 | { | ||
| 2135 |
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|
48 | PetscFunctionBegin; |
| 2136 |
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48 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2137 |
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48 | PetscAssertPointer(lock,2); |
| 2138 |
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|
48 | PetscUseMethod(svd,"SVDTRLanczosGetLocking_C",(SVD,PetscBool*),(svd,lock)); |
| 2139 |
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|
48 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2140 | } | ||
| 2141 | |||
| 2142 | 104 | static PetscErrorCode SVDTRLanczosSetExplicitMatrix_TRLanczos(SVD svd,PetscBool explicitmat) | |
| 2143 | { | ||
| 2144 | 104 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2145 | |||
| 2146 |
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104 | PetscFunctionBegin; |
| 2147 |
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|
104 | if (lanczos->explicitmatrix != explicitmat) { |
| 2148 | 82 | lanczos->explicitmatrix = explicitmat; | |
| 2149 | 82 | svd->state = SVD_STATE_INITIAL; | |
| 2150 | } | ||
| 2151 |
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|
104 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2152 | } | ||
| 2153 | |||
| 2154 | /*@ | ||
| 2155 | SVDTRLanczosSetExplicitMatrix - Indicate if the stacked matrix | ||
| 2156 | $Z=[A^*,B^*]^*$ must be built explicitly. | ||
| 2157 | |||
| 2158 | Logically Collective | ||
| 2159 | |||
| 2160 | Input Parameters: | ||
| 2161 | + svd - the singular value solver context | ||
| 2162 | - explicitmat - Boolean flag indicating if $Z$ is built explicitly | ||
| 2163 | |||
| 2164 | Options Database Key: | ||
| 2165 | . -svd_trlanczos_explicitmatrix - enable the explicit construction of the stacked matrix | ||
| 2166 | |||
| 2167 | Note: | ||
| 2168 | This option is relevant for the GSVD case only. | ||
| 2169 | $Z$ is the coefficient matrix of the `KSP` solver used internally. | ||
| 2170 | |||
| 2171 | Level: advanced | ||
| 2172 | |||
| 2173 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetExplicitMatrix()` | ||
| 2174 | @*/ | ||
| 2175 | 104 | PetscErrorCode SVDTRLanczosSetExplicitMatrix(SVD svd,PetscBool explicitmat) | |
| 2176 | { | ||
| 2177 |
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104 | PetscFunctionBegin; |
| 2178 |
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|
104 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2179 |
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|
104 | PetscValidLogicalCollectiveBool(svd,explicitmat,2); |
| 2180 |
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|
104 | PetscTryMethod(svd,"SVDTRLanczosSetExplicitMatrix_C",(SVD,PetscBool),(svd,explicitmat)); |
| 2181 |
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|
104 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2182 | } | ||
| 2183 | |||
| 2184 | ✗ | static PetscErrorCode SVDTRLanczosGetExplicitMatrix_TRLanczos(SVD svd,PetscBool *explicitmat) | |
| 2185 | { | ||
| 2186 | ✗ | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2187 | |||
| 2188 | ✗ | PetscFunctionBegin; | |
| 2189 | ✗ | *explicitmat = lanczos->explicitmatrix; | |
| 2190 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 2191 | } | ||
| 2192 | |||
| 2193 | /*@ | ||
| 2194 | SVDTRLanczosGetExplicitMatrix - Returns the flag indicating if the stacked matrix | ||
| 2195 | $Z=[A^*,B^*]^*$ is built explicitly. | ||
| 2196 | |||
| 2197 | Not Collective | ||
| 2198 | |||
| 2199 | Input Parameter: | ||
| 2200 | . svd - the singular value solver context | ||
| 2201 | |||
| 2202 | Output Parameter: | ||
| 2203 | . explicitmat - the mode flag | ||
| 2204 | |||
| 2205 | Level: advanced | ||
| 2206 | |||
| 2207 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetExplicitMatrix()` | ||
| 2208 | @*/ | ||
| 2209 | ✗ | PetscErrorCode SVDTRLanczosGetExplicitMatrix(SVD svd,PetscBool *explicitmat) | |
| 2210 | { | ||
| 2211 | ✗ | PetscFunctionBegin; | |
| 2212 | ✗ | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); | |
| 2213 | ✗ | PetscAssertPointer(explicitmat,2); | |
| 2214 | ✗ | PetscUseMethod(svd,"SVDTRLanczosGetExplicitMatrix_C",(SVD,PetscBool*),(svd,explicitmat)); | |
| 2215 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 2216 | } | ||
| 2217 | |||
| 2218 | 168 | static PetscErrorCode SVDTRLanczosSetScale_TRLanczos(SVD svd,PetscReal scale) | |
| 2219 | { | ||
| 2220 | 168 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 2221 | |||
| 2222 |
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168 | PetscFunctionBegin; |
| 2223 |
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|
168 | if (scale<0) { |
| 2224 | 72 | ctx->scalef = 1.0; | |
| 2225 | 72 | ctx->scaleth = -scale; | |
| 2226 | } else { | ||
| 2227 | 96 | ctx->scalef = scale; | |
| 2228 | 96 | ctx->scaleth = 0.0; | |
| 2229 | } | ||
| 2230 |
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|
168 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2231 | } | ||
| 2232 | |||
| 2233 | /*@ | ||
| 2234 | SVDTRLanczosSetScale - Sets the scale parameter for the GSVD. | ||
| 2235 | |||
| 2236 | Logically Collective | ||
| 2237 | |||
| 2238 | Input Parameters: | ||
| 2239 | + svd - the singular value solver context | ||
| 2240 | - scale - scale parameter | ||
| 2241 | |||
| 2242 | Options Database Key: | ||
| 2243 | . -svd_trlanczos_scale \<scale\> - scale factor/threshold | ||
| 2244 | |||
| 2245 | Notes: | ||
| 2246 | This parameter is relevant for the GSVD case only. If the parameter is | ||
| 2247 | positive, it indicates the scale factor to multiply $B$ in the stacked matrix | ||
| 2248 | $Z=[A^*,B^*]^*$. If | ||
| 2249 | negative, its absolute value is the threshold for automatic scaling. | ||
| 2250 | In automatic scaling, whenever the largest approximate generalized singular | ||
| 2251 | value (or the inverse of the smallest value, if `SVD_SMALLEST` is used) | ||
| 2252 | exceeds the threshold, the computation is restarted with matrix $B$ | ||
| 2253 | scaled by that value. | ||
| 2254 | |||
| 2255 | Level: advanced | ||
| 2256 | |||
| 2257 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosGetScale()` | ||
| 2258 | @*/ | ||
| 2259 | 168 | PetscErrorCode SVDTRLanczosSetScale(SVD svd,PetscReal scale) | |
| 2260 | { | ||
| 2261 |
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168 | PetscFunctionBegin; |
| 2262 |
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168 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2263 |
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|
168 | PetscValidLogicalCollectiveReal(svd,scale,2); |
| 2264 |
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168 | PetscTryMethod(svd,"SVDTRLanczosSetScale_C",(SVD,PetscReal),(svd,scale)); |
| 2265 |
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|
168 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2266 | } | ||
| 2267 | |||
| 2268 | 804 | static PetscErrorCode SVDTRLanczosGetScale_TRLanczos(SVD svd,PetscReal *scale) | |
| 2269 | { | ||
| 2270 | 804 | SVD_TRLANCZOS *ctx = (SVD_TRLANCZOS*)svd->data; | |
| 2271 | |||
| 2272 |
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804 | PetscFunctionBegin; |
| 2273 |
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|
804 | if (ctx->scaleth==0) *scale = ctx->scalef; |
| 2274 | 48 | else *scale = -ctx->scaleth; | |
| 2275 |
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|
804 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2276 | } | ||
| 2277 | |||
| 2278 | /*@ | ||
| 2279 | SVDTRLanczosGetScale - Gets the scale parameter for the GSVD. | ||
| 2280 | |||
| 2281 | Not Collective | ||
| 2282 | |||
| 2283 | Input Parameter: | ||
| 2284 | . svd - the singular value solver context | ||
| 2285 | |||
| 2286 | Output Parameter: | ||
| 2287 | . scale - the scale parameter | ||
| 2288 | |||
| 2289 | Level: advanced | ||
| 2290 | |||
| 2291 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetScale()` | ||
| 2292 | @*/ | ||
| 2293 | 804 | PetscErrorCode SVDTRLanczosGetScale(SVD svd,PetscReal *scale) | |
| 2294 | { | ||
| 2295 |
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804 | PetscFunctionBegin; |
| 2296 |
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804 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 2297 |
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804 | PetscAssertPointer(scale,2); |
| 2298 |
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804 | PetscUseMethod(svd,"SVDTRLanczosGetScale_C",(SVD,PetscReal*),(svd,scale)); |
| 2299 |
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|
804 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2300 | } | ||
| 2301 | |||
| 2302 | 772 | static PetscErrorCode SVDReset_TRLanczos(SVD svd) | |
| 2303 | { | ||
| 2304 | 772 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2305 | |||
| 2306 |
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772 | PetscFunctionBegin; |
| 2307 |
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|
772 | if (svd->isgeneralized || (!svd->problem_type && svd->OPb)) { |
| 2308 |
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|
445 | PetscCall(KSPReset(lanczos->ksp)); |
| 2309 |
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|
445 | PetscCall(MatDestroy(&lanczos->Z)); |
| 2310 | } | ||
| 2311 |
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|
194 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2312 | } | ||
| 2313 | |||
| 2314 | 773 | static PetscErrorCode SVDDestroy_TRLanczos(SVD svd) | |
| 2315 | { | ||
| 2316 | 773 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2317 | |||
| 2318 |
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773 | PetscFunctionBegin; |
| 2319 |
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|
773 | if (svd->isgeneralized || (!svd->problem_type && svd->OPb)) PetscCall(KSPDestroy(&lanczos->ksp)); |
| 2320 |
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|
773 | PetscCall(PetscFree(svd->data)); |
| 2321 |
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|
773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetOneSide_C",NULL)); |
| 2322 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetOneSide_C",NULL)); |
| 2323 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetGBidiag_C",NULL)); |
| 2324 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetGBidiag_C",NULL)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetKSP_C",NULL)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetKSP_C",NULL)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetRestart_C",NULL)); |
| 2328 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetRestart_C",NULL)); |
| 2329 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetLocking_C",NULL)); |
| 2330 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetLocking_C",NULL)); |
| 2331 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetExplicitMatrix_C",NULL)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetExplicitMatrix_C",NULL)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetScale_C",NULL)); |
| 2334 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetScale_C",NULL)); |
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|
192 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2336 | } | ||
| 2337 | |||
| 2338 | 8 | static PetscErrorCode SVDView_TRLanczos(SVD svd,PetscViewer viewer) | |
| 2339 | { | ||
| 2340 | 8 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2341 | 8 | PetscBool isascii; | |
| 2342 | |||
| 2343 |
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8 | PetscFunctionBegin; |
| 2344 |
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8 | PetscCall(PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii)); |
| 2345 |
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8 | if (isascii) { |
| 2346 |
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8 | PetscCall(PetscViewerASCIIPrintf(viewer," %d%% of basis vectors kept after restart\n",(int)(100*lanczos->keep))); |
| 2347 |
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8 | PetscCall(PetscViewerASCIIPrintf(viewer," using the %slocking variant\n",lanczos->lock?"":"non-")); |
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8 | if (svd->isgeneralized) { |
| 2349 | ✗ | const char *bidiag=""; | |
| 2350 | |||
| 2351 | ✗ | switch (lanczos->bidiag) { | |
| 2352 | ✗ | case SVD_TRLANCZOS_GBIDIAG_SINGLE: bidiag = "single"; break; | |
| 2353 | ✗ | case SVD_TRLANCZOS_GBIDIAG_UPPER: bidiag = "joint upper-upper"; break; | |
| 2354 | ✗ | case SVD_TRLANCZOS_GBIDIAG_LOWER: bidiag = "joint lower-upper"; break; | |
| 2355 | } | ||
| 2356 | ✗ | PetscCall(PetscViewerASCIIPrintf(viewer," bidiagonalization choice: %s\n",bidiag)); | |
| 2357 | ✗ | PetscCall(PetscViewerASCIIPrintf(viewer," %s matrix\n",lanczos->explicitmatrix?"explicit":"implicit")); | |
| 2358 | ✗ | if (lanczos->scaleth==0) PetscCall(PetscViewerASCIIPrintf(viewer," scale factor for matrix B: %g\n",(double)lanczos->scalef)); | |
| 2359 | ✗ | else PetscCall(PetscViewerASCIIPrintf(viewer," automatic scaling for matrix B with threshold: %g\n",(double)lanczos->scaleth)); | |
| 2360 | ✗ | if (!lanczos->ksp) PetscCall(SVDTRLanczosGetKSP(svd,&lanczos->ksp)); | |
| 2361 | ✗ | PetscCall(PetscViewerASCIIPushTab(viewer)); | |
| 2362 | ✗ | PetscCall(KSPView(lanczos->ksp,viewer)); | |
| 2363 | ✗ | PetscCall(PetscViewerASCIIPopTab(viewer)); | |
| 2364 |
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|
16 | } else PetscCall(PetscViewerASCIIPrintf(viewer," %s-sided reorthogonalization\n",lanczos->oneside? "one": "two")); |
| 2365 | } | ||
| 2366 |
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2 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2367 | } | ||
| 2368 | |||
| 2369 | 1620 | static PetscErrorCode SVDSetDSType_TRLanczos(SVD svd) | |
| 2370 | { | ||
| 2371 | 1620 | SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS*)svd->data; | |
| 2372 | 1620 | DSType dstype; | |
| 2373 | |||
| 2374 |
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1620 | PetscFunctionBegin; |
| 2375 |
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1620 | dstype = svd->ishyperbolic? DSHSVD: DSSVD; |
| 2376 |
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1620 | if (svd->OPb && (lanczos->bidiag==SVD_TRLANCZOS_GBIDIAG_UPPER || lanczos->bidiag==SVD_TRLANCZOS_GBIDIAG_LOWER)) dstype = DSGSVD; |
| 2377 |
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1620 | PetscCall(DSSetType(svd->ds,dstype)); |
| 2378 |
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406 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2379 | } | ||
| 2380 | |||
| 2381 | /*MC | ||
| 2382 | SVDTRLANCZOS - SVDTRLANCZOS = "trlanczos" - A thick-restart Golub-Kahan-Lanczos | ||
| 2383 | bidiagonalization method. | ||
| 2384 | |||
| 2385 | Notes: | ||
| 2386 | The implemented method is Lanczos bidiagonalization with thick restart. It should | ||
| 2387 | generally provide much better convergence than the plain Lanczos in `SVDLANCZOS`. | ||
| 2388 | |||
| 2389 | The implementation includes a one-sided orthogonalization option, and | ||
| 2390 | efficient parallel orthogonalization, see the details in {cite:p}`Her07c,Her08`. | ||
| 2391 | |||
| 2392 | This solver also provides support for the GSVD (see details in {cite:p}`Alv24`) | ||
| 2393 | and the HSVD. | ||
| 2394 | |||
| 2395 | Level: beginner | ||
| 2396 | |||
| 2397 | .seealso: [](ch:svd), [](#sec:svdback), `SVD`, `SVDType`, `SVDSetType()`, `SVDSetProblemType()`, `SVDTRLANCZOS` | ||
| 2398 | M*/ | ||
| 2399 | 773 | SLEPC_EXTERN PetscErrorCode SVDCreate_TRLanczos(SVD svd) | |
| 2400 | { | ||
| 2401 | 773 | SVD_TRLANCZOS *ctx; | |
| 2402 | |||
| 2403 |
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773 | PetscFunctionBegin; |
| 2404 |
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773 | PetscCall(PetscNew(&ctx)); |
| 2405 | 773 | svd->data = (void*)ctx; | |
| 2406 | |||
| 2407 | 773 | ctx->lock = PETSC_TRUE; | |
| 2408 | 773 | ctx->bidiag = SVD_TRLANCZOS_GBIDIAG_LOWER; | |
| 2409 | 773 | ctx->scalef = 1.0; | |
| 2410 | 773 | ctx->scaleth = 0.0; | |
| 2411 | |||
| 2412 | 773 | svd->ops->setup = SVDSetUp_TRLanczos; | |
| 2413 | 773 | svd->ops->solve = SVDSolve_TRLanczos; | |
| 2414 | 773 | svd->ops->solveg = SVDSolve_TRLanczos_GSVD; | |
| 2415 | 773 | svd->ops->solveh = SVDSolve_TRLanczos_HSVD; | |
| 2416 | 773 | svd->ops->destroy = SVDDestroy_TRLanczos; | |
| 2417 | 773 | svd->ops->reset = SVDReset_TRLanczos; | |
| 2418 | 773 | svd->ops->setfromoptions = SVDSetFromOptions_TRLanczos; | |
| 2419 | 773 | svd->ops->view = SVDView_TRLanczos; | |
| 2420 | 773 | svd->ops->setdstype = SVDSetDSType_TRLanczos; | |
| 2421 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetOneSide_C",SVDTRLanczosSetOneSide_TRLanczos)); |
| 2422 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetOneSide_C",SVDTRLanczosGetOneSide_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetGBidiag_C",SVDTRLanczosSetGBidiag_TRLanczos)); |
| 2424 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetGBidiag_C",SVDTRLanczosGetGBidiag_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetKSP_C",SVDTRLanczosSetKSP_TRLanczos)); |
| 2426 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetKSP_C",SVDTRLanczosGetKSP_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetRestart_C",SVDTRLanczosSetRestart_TRLanczos)); |
| 2428 |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetRestart_C",SVDTRLanczosGetRestart_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetLocking_C",SVDTRLanczosSetLocking_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetLocking_C",SVDTRLanczosGetLocking_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetExplicitMatrix_C",SVDTRLanczosSetExplicitMatrix_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetExplicitMatrix_C",SVDTRLanczosGetExplicitMatrix_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosSetScale_C",SVDTRLanczosSetScale_TRLanczos)); |
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773 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDTRLanczosGetScale_C",SVDTRLanczosGetScale_TRLanczos)); |
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192 | PetscFunctionReturn(PETSC_SUCCESS); |
| 2436 | } | ||
| 2437 |