| 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: "lanczos" | ||
| 12 | |||
| 13 | Method: Explicitly restarted Lanczos | ||
| 14 | |||
| 15 | Algorithm: | ||
| 16 | |||
| 17 | Golub-Kahan-Lanczos bidiagonalization with explicit 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 | |||
| 33 | typedef struct { | ||
| 34 | PetscBool oneside; | ||
| 35 | } SVD_LANCZOS; | ||
| 36 | |||
| 37 | 204 | static PetscErrorCode SVDSetUp_Lanczos(SVD svd) | |
| 38 | { | ||
| 39 | 204 | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 40 | 204 | PetscInt N; | |
| 41 | |||
| 42 |
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204 | PetscFunctionBegin; |
| 43 |
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204 | SVDCheckStandard(svd); |
| 44 |
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204 | SVDCheckDefinite(svd); |
| 45 |
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204 | PetscCall(MatGetSize(svd->A,NULL,&N)); |
| 46 |
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204 | PetscCall(SVDSetDimensions_Default(svd)); |
| 47 |
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204 | PetscCheck(svd->ncv<=svd->nsv+svd->mpd,PetscObjectComm((PetscObject)svd),PETSC_ERR_USER_INPUT,"The value of ncv must not be larger than nev+mpd"); |
| 48 |
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303 | if (svd->max_it==PETSC_DETERMINE) svd->max_it = PetscMax(N/svd->ncv,100)*((svd->stop==SVD_STOP_THRESHOLD)?10:1); |
| 49 | 204 | svd->leftbasis = PetscNot(lanczos->oneside); | |
| 50 |
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204 | PetscCall(SVDAllocateSolution(svd,1)); |
| 51 |
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204 | PetscCall(DSSetType(svd->ds,DSSVD)); |
| 52 |
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204 | PetscCall(DSSetCompact(svd->ds,PETSC_TRUE)); |
| 53 |
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204 | PetscCall(DSSetExtraRow(svd->ds,PETSC_TRUE)); |
| 54 |
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204 | PetscCall(DSAllocate(svd->ds,svd->ncv+1)); |
| 55 |
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38 | PetscFunctionReturn(PETSC_SUCCESS); |
| 56 | } | ||
| 57 | |||
| 58 | 13760 | PetscErrorCode SVDTwoSideLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,BV U,PetscInt k,PetscInt *n,PetscBool *breakdown) | |
| 59 | { | ||
| 60 | 13760 | PetscInt i; | |
| 61 | 13760 | Vec u,v; | |
| 62 | 13760 | PetscBool lindep=PETSC_FALSE; | |
| 63 | |||
| 64 |
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13760 | PetscFunctionBegin; |
| 65 |
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13760 | PetscCall(BVGetColumn(svd->V,k,&v)); |
| 66 |
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13760 | PetscCall(BVGetColumn(svd->U,k,&u)); |
| 67 |
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13760 | PetscCall(MatMult(svd->A,v,u)); |
| 68 |
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13760 | PetscCall(BVRestoreColumn(svd->V,k,&v)); |
| 69 |
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13760 | PetscCall(BVRestoreColumn(svd->U,k,&u)); |
| 70 |
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13760 | PetscCall(BVOrthonormalizeColumn(svd->U,k,PETSC_FALSE,alpha+k,&lindep)); |
| 71 |
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13760 | if (PetscUnlikely(lindep)) { |
| 72 | ✗ | *n = k; | |
| 73 | ✗ | if (breakdown) *breakdown = lindep; | |
| 74 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 75 | } | ||
| 76 | |||
| 77 |
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96165 | for (i=k+1;i<*n;i++) { |
| 78 |
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82405 | PetscCall(BVGetColumn(svd->V,i,&v)); |
| 79 |
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82405 | PetscCall(BVGetColumn(svd->U,i-1,&u)); |
| 80 |
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82405 | PetscCall(MatMult(svd->AT,u,v)); |
| 81 |
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82405 | PetscCall(BVRestoreColumn(svd->V,i,&v)); |
| 82 |
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82405 | PetscCall(BVRestoreColumn(svd->U,i-1,&u)); |
| 83 |
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82405 | PetscCall(BVOrthonormalizeColumn(svd->V,i,PETSC_FALSE,beta+i-1,&lindep)); |
| 84 |
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82405 | if (PetscUnlikely(lindep)) { |
| 85 | ✗ | *n = i; | |
| 86 | ✗ | break; | |
| 87 | } | ||
| 88 |
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82405 | PetscCall(BVGetColumn(svd->V,i,&v)); |
| 89 |
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82405 | PetscCall(BVGetColumn(svd->U,i,&u)); |
| 90 |
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82405 | PetscCall(MatMult(svd->A,v,u)); |
| 91 |
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82405 | PetscCall(BVRestoreColumn(svd->V,i,&v)); |
| 92 |
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82405 | PetscCall(BVRestoreColumn(svd->U,i,&u)); |
| 93 |
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82405 | PetscCall(BVOrthonormalizeColumn(svd->U,i,PETSC_FALSE,alpha+i,&lindep)); |
| 94 |
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82405 | if (PetscUnlikely(lindep)) { |
| 95 | ✗ | *n = i; | |
| 96 | ✗ | break; | |
| 97 | } | ||
| 98 | } | ||
| 99 | |||
| 100 |
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13760 | if (!lindep) { |
| 101 |
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13760 | PetscCall(BVGetColumn(svd->V,*n,&v)); |
| 102 |
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13760 | PetscCall(BVGetColumn(svd->U,*n-1,&u)); |
| 103 |
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13760 | PetscCall(MatMult(svd->AT,u,v)); |
| 104 |
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13760 | PetscCall(BVRestoreColumn(svd->V,*n,&v)); |
| 105 |
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13760 | PetscCall(BVRestoreColumn(svd->U,*n-1,&u)); |
| 106 |
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13760 | PetscCall(BVOrthogonalizeColumn(svd->V,*n,NULL,beta+*n-1,&lindep)); |
| 107 | } | ||
| 108 |
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13760 | if (breakdown) *breakdown = lindep; |
| 109 |
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2396 | PetscFunctionReturn(PETSC_SUCCESS); |
| 110 | } | ||
| 111 | |||
| 112 | 688 | static PetscErrorCode SVDOneSideLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,Vec u,Vec u_1,PetscInt k,PetscInt n,PetscScalar* work) | |
| 113 | { | ||
| 114 | 688 | PetscInt i,bvl,bvk; | |
| 115 | 688 | PetscReal a,b; | |
| 116 | 688 | Vec z,temp; | |
| 117 | |||
| 118 |
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688 | PetscFunctionBegin; |
| 119 |
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688 | PetscCall(BVGetActiveColumns(V,&bvl,&bvk)); |
| 120 |
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688 | PetscCall(BVGetColumn(V,k,&z)); |
| 121 |
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688 | PetscCall(MatMult(svd->A,z,u)); |
| 122 |
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688 | PetscCall(BVRestoreColumn(V,k,&z)); |
| 123 | |||
| 124 |
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5980 | for (i=k+1;i<n;i++) { |
| 125 |
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5292 | PetscCall(BVGetColumn(V,i,&z)); |
| 126 |
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5292 | PetscCall(MatMult(svd->AT,u,z)); |
| 127 |
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5292 | PetscCall(BVRestoreColumn(V,i,&z)); |
| 128 |
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5292 | PetscCall(VecNormBegin(u,NORM_2,&a)); |
| 129 |
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5292 | PetscCall(BVSetActiveColumns(V,0,i)); |
| 130 |
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5292 | PetscCall(BVDotColumnBegin(V,i,work)); |
| 131 |
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5292 | PetscCall(VecNormEnd(u,NORM_2,&a)); |
| 132 |
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5292 | PetscCall(BVDotColumnEnd(V,i,work)); |
| 133 |
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5292 | PetscCall(VecScale(u,1.0/a)); |
| 134 |
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5292 | PetscCall(BVMultColumn(V,-1.0/a,1.0/a,i,work)); |
| 135 | |||
| 136 | /* h = V^* z, z = z - V h */ | ||
| 137 |
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5292 | PetscCall(BVDotColumn(V,i,work)); |
| 138 |
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5292 | PetscCall(BVMultColumn(V,-1.0,1.0,i,work)); |
| 139 |
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5292 | PetscCall(BVNormColumn(V,i,NORM_2,&b)); |
| 140 |
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5292 | PetscCheck(PetscAbsReal(b)>10*PETSC_MACHINE_EPSILON,PetscObjectComm((PetscObject)svd),PETSC_ERR_PLIB,"Recurrence generated a zero vector; use a two-sided variant"); |
| 141 |
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5292 | PetscCall(BVScaleColumn(V,i,1.0/b)); |
| 142 | |||
| 143 |
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5292 | PetscCall(BVGetColumn(V,i,&z)); |
| 144 |
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5292 | PetscCall(MatMult(svd->A,z,u_1)); |
| 145 |
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5292 | PetscCall(BVRestoreColumn(V,i,&z)); |
| 146 |
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5292 | PetscCall(VecAXPY(u_1,-b,u)); |
| 147 | 5292 | alpha[i-1] = a; | |
| 148 | 5292 | beta[i-1] = b; | |
| 149 | 5292 | temp = u; | |
| 150 | 5292 | u = u_1; | |
| 151 | 5292 | u_1 = temp; | |
| 152 | } | ||
| 153 | |||
| 154 |
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688 | PetscCall(BVGetColumn(V,n,&z)); |
| 155 |
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688 | PetscCall(MatMult(svd->AT,u,z)); |
| 156 |
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688 | PetscCall(BVRestoreColumn(V,n,&z)); |
| 157 |
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688 | PetscCall(VecNormBegin(u,NORM_2,&a)); |
| 158 |
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688 | PetscCall(BVDotColumnBegin(V,n,work)); |
| 159 |
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688 | PetscCall(VecNormEnd(u,NORM_2,&a)); |
| 160 |
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688 | PetscCall(BVDotColumnEnd(V,n,work)); |
| 161 |
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688 | PetscCall(VecScale(u,1.0/a)); |
| 162 |
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688 | PetscCall(BVMultColumn(V,-1.0/a,1.0/a,n,work)); |
| 163 | |||
| 164 | /* h = V^* z, z = z - V h */ | ||
| 165 |
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688 | PetscCall(BVDotColumn(V,n,work)); |
| 166 |
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688 | PetscCall(BVMultColumn(V,-1.0,1.0,n,work)); |
| 167 |
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688 | PetscCall(BVNormColumn(V,i,NORM_2,&b)); |
| 168 | |||
| 169 | 688 | alpha[n-1] = a; | |
| 170 | 688 | beta[n-1] = b; | |
| 171 |
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688 | PetscCall(BVSetActiveColumns(V,bvl,bvk)); |
| 172 |
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108 | PetscFunctionReturn(PETSC_SUCCESS); |
| 173 | } | ||
| 174 | |||
| 175 | /* | ||
| 176 | SVDKrylovConvergence - Implements the loop that checks for convergence | ||
| 177 | in Krylov methods. | ||
| 178 | |||
| 179 | Input Parameters: | ||
| 180 | svd - the solver; some error estimates are updated in svd->errest | ||
| 181 | getall - whether all residuals must be computed | ||
| 182 | kini - initial value of k (the loop variable) | ||
| 183 | nits - number of iterations of the loop | ||
| 184 | normr - norm of triangular factor of qr([A;B]), used only in GSVD | ||
| 185 | |||
| 186 | Output Parameter: | ||
| 187 | kout - the first index where the convergence test failed | ||
| 188 | */ | ||
| 189 | 25876 | PetscErrorCode SVDKrylovConvergence(SVD svd,PetscBool getall,PetscInt kini,PetscInt nits,PetscReal normr,PetscInt *kout) | |
| 190 | { | ||
| 191 | 25876 | PetscInt k,marker,ld; | |
| 192 | 25876 | PetscReal *alpha,*beta,*betah,resnorm; | |
| 193 | 25876 | PetscBool extra; | |
| 194 | |||
| 195 |
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25876 | PetscFunctionBegin; |
| 196 |
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25876 | if (PetscUnlikely(svd->conv == SVD_CONV_MAXIT && svd->its >= svd->max_it)) *kout = svd->nsv; |
| 197 | else { | ||
| 198 |
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25876 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 199 |
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25876 | PetscCall(DSGetExtraRow(svd->ds,&extra)); |
| 200 |
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25876 | PetscCheck(extra,PetscObjectComm((PetscObject)svd),PETSC_ERR_SUP,"Only implemented for DS with extra row"); |
| 201 | 25876 | marker = -1; | |
| 202 |
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25876 | if (svd->trackall) getall = PETSC_TRUE; |
| 203 |
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25876 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 204 | 25876 | beta = alpha + ld; | |
| 205 | 25876 | betah = alpha + 2*ld; | |
| 206 |
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32637 | for (k=kini;k<kini+nits;k++) { |
| 207 |
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32588 | if (svd->isgeneralized) resnorm = SlepcAbs(beta[k],betah[k])*normr; |
| 208 | 21416 | else resnorm = PetscAbsReal(beta[k]); | |
| 209 |
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32588 | PetscCall((*svd->converged)(svd,svd->sigma[k],resnorm,&svd->errest[k],svd->convergedctx)); |
| 210 |
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32588 | if (marker==-1 && svd->errest[k] >= svd->tol) marker = k; |
| 211 |
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32588 | if (marker!=-1 && !getall) break; |
| 212 | } | ||
| 213 |
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25876 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 214 |
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25876 | if (marker!=-1) k = marker; |
| 215 | 25876 | *kout = k; | |
| 216 | } | ||
| 217 |
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4830 | PetscFunctionReturn(PETSC_SUCCESS); |
| 218 | } | ||
| 219 | |||
| 220 | 204 | static PetscErrorCode SVDSolve_Lanczos(SVD svd) | |
| 221 | { | ||
| 222 | 204 | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 223 | 204 | PetscReal *alpha,*beta; | |
| 224 | 204 | PetscScalar *swork,*w,*P,*aux1,*aux2; | |
| 225 | 204 | PetscInt i,k,j,nv,ld; | |
| 226 | 204 | Vec u=NULL,u_1=NULL; | |
| 227 | 204 | Mat U,V; | |
| 228 | |||
| 229 |
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204 | PetscFunctionBegin; |
| 230 | /* allocate working space */ | ||
| 231 |
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204 | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); |
| 232 |
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204 | PetscCall(PetscMalloc2(ld,&w,svd->ncv,&swork)); |
| 233 | |||
| 234 |
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204 | if (lanczos->oneside) { |
| 235 |
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24 | PetscCall(MatCreateVecs(svd->A,NULL,&u)); |
| 236 |
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24 | PetscCall(MatCreateVecs(svd->A,NULL,&u_1)); |
| 237 | } | ||
| 238 | |||
| 239 | /* normalize start vector */ | ||
| 240 |
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204 | if (!svd->nini) { |
| 241 |
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156 | PetscCall(BVSetRandomColumn(svd->V,0)); |
| 242 |
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156 | PetscCall(BVOrthonormalizeColumn(svd->V,0,PETSC_TRUE,NULL,NULL)); |
| 243 | } | ||
| 244 | |||
| 245 | 6462 | while (svd->reason == SVD_CONVERGED_ITERATING) { | |
| 246 | 6258 | svd->its++; | |
| 247 | |||
| 248 | /* inner loop */ | ||
| 249 | 6258 | nv = PetscMin(svd->nconv+svd->mpd,svd->ncv); | |
| 250 |
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6258 | PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 251 | 6258 | beta = alpha + ld; | |
| 252 |
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6258 | if (lanczos->oneside) PetscCall(SVDOneSideLanczos(svd,alpha,beta,svd->V,u,u_1,svd->nconv,nv,swork)); |
| 253 | else { | ||
| 254 |
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5570 | PetscCall(SVDTwoSideLanczos(svd,alpha,beta,svd->V,svd->U,svd->nconv,&nv,NULL)); |
| 255 |
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5570 | PetscCall(BVSetActiveColumns(svd->U,svd->nconv,nv)); |
| 256 | } | ||
| 257 |
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6258 | PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha)); |
| 258 |
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6258 | PetscCall(BVSetActiveColumns(svd->V,svd->nconv,nv)); |
| 259 | |||
| 260 | /* compute SVD of bidiagonal matrix */ | ||
| 261 |
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6258 | PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,0)); |
| 262 |
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6258 | PetscCall(DSSVDSetDimensions(svd->ds,nv)); |
| 263 |
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6258 | PetscCall(DSSetState(svd->ds,DS_STATE_INTERMEDIATE)); |
| 264 |
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6258 | PetscCall(DSSolve(svd->ds,w,NULL)); |
| 265 |
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6258 | PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL)); |
| 266 |
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6258 | PetscCall(DSUpdateExtraRow(svd->ds)); |
| 267 |
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6258 | PetscCall(DSSynchronize(svd->ds,w,NULL)); |
| 268 |
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65625 | for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]); |
| 269 | |||
| 270 | /* check convergence */ | ||
| 271 |
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6258 | PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,1.0,&k)); |
| 272 |
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6258 | SVDSetCtxThreshold(svd,svd->sigma,svd->errest,k,nv); |
| 273 |
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6258 | PetscCall((*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx)); |
| 274 | |||
| 275 | /* compute restart vector */ | ||
| 276 |
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6258 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 277 |
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6054 | if (k<nv) { |
| 278 |
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6054 | PetscCall(DSGetArray(svd->ds,DS_MAT_V,&P)); |
| 279 |
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63617 | for (j=svd->nconv;j<nv;j++) swork[j-svd->nconv] = PetscConj(P[j+k*ld]); |
| 280 |
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6054 | PetscCall(DSRestoreArray(svd->ds,DS_MAT_V,&P)); |
| 281 |
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6054 | PetscCall(BVMultColumn(svd->V,1.0,0.0,nv,swork)); |
| 282 | } else { | ||
| 283 | /* all approximations have converged, generate a new initial vector */ | ||
| 284 | ✗ | PetscCall(BVSetRandomColumn(svd->V,nv)); | |
| 285 | ✗ | PetscCall(BVOrthonormalizeColumn(svd->V,nv,PETSC_FALSE,NULL,NULL)); | |
| 286 | } | ||
| 287 | } | ||
| 288 | |||
| 289 | /* compute converged singular vectors */ | ||
| 290 |
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6258 | PetscCall(DSGetMat(svd->ds,DS_MAT_V,&V)); |
| 291 |
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6258 | PetscCall(BVMultInPlace(svd->V,V,svd->nconv,k)); |
| 292 |
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6258 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&V)); |
| 293 |
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6258 | if (!lanczos->oneside) { |
| 294 |
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5570 | PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U)); |
| 295 |
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5570 | PetscCall(BVMultInPlace(svd->U,U,svd->nconv,k)); |
| 296 |
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5570 | PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U)); |
| 297 | } | ||
| 298 | |||
| 299 |
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6258 | if (svd->reason == SVD_CONVERGED_ITERATING) { |
| 300 |
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6054 | PetscCall(BVCopyColumn(svd->V,nv,k)); /* copy restart vector from the last column */ |
| 301 |
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6054 | if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */ |
| 302 | ✗ | svd->ncv = svd->mpd+k; | |
| 303 | ✗ | PetscCall(SVDReallocateSolution(svd,svd->ncv+1)); | |
| 304 | ✗ | for (i=nv;i<svd->ncv;i++) svd->perm[i] = i; | |
| 305 | ✗ | PetscCall(DSReallocate(svd->ds,svd->ncv+1)); | |
| 306 | ✗ | aux1 = w; | |
| 307 | ✗ | aux2 = swork; | |
| 308 | ✗ | PetscCall(PetscMalloc2(svd->ncv+1,&w,svd->ncv,&swork)); | |
| 309 | ✗ | PetscCall(PetscArraycpy(w,aux1,ld)); | |
| 310 | ✗ | PetscCall(PetscFree2(aux1,aux2)); | |
| 311 | ✗ | PetscCall(DSGetLeadingDimension(svd->ds,&ld)); | |
| 312 | } | ||
| 313 | } | ||
| 314 | |||
| 315 | 6258 | svd->nconv = k; | |
| 316 |
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6462 | PetscCall(SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,nv)); |
| 317 | } | ||
| 318 | |||
| 319 | /* free working space */ | ||
| 320 |
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204 | PetscCall(VecDestroy(&u)); |
| 321 |
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204 | PetscCall(VecDestroy(&u_1)); |
| 322 |
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204 | PetscCall(PetscFree2(w,swork)); |
| 323 |
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38 | PetscFunctionReturn(PETSC_SUCCESS); |
| 324 | } | ||
| 325 | |||
| 326 | 144 | static PetscErrorCode SVDSetFromOptions_Lanczos(SVD svd,PetscOptionItems PetscOptionsObject) | |
| 327 | { | ||
| 328 | 144 | PetscBool set,val; | |
| 329 | 144 | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 330 | |||
| 331 |
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144 | PetscFunctionBegin; |
| 332 |
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144 | PetscOptionsHeadBegin(PetscOptionsObject,"SVD Lanczos Options"); |
| 333 | |||
| 334 |
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144 | PetscCall(PetscOptionsBool("-svd_lanczos_oneside","Use one-side reorthogonalization","SVDLanczosSetOneSide",lanczos->oneside,&val,&set)); |
| 335 |
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144 | if (set) PetscCall(SVDLanczosSetOneSide(svd,val)); |
| 336 | |||
| 337 |
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144 | PetscOptionsHeadEnd(); |
| 338 |
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26 | PetscFunctionReturn(PETSC_SUCCESS); |
| 339 | } | ||
| 340 | |||
| 341 | 24 | static PetscErrorCode SVDLanczosSetOneSide_Lanczos(SVD svd,PetscBool oneside) | |
| 342 | { | ||
| 343 | 24 | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 344 | |||
| 345 |
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24 | PetscFunctionBegin; |
| 346 |
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24 | if (lanczos->oneside != oneside) { |
| 347 | 24 | lanczos->oneside = oneside; | |
| 348 | 24 | svd->state = SVD_STATE_INITIAL; | |
| 349 | } | ||
| 350 |
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24 | PetscFunctionReturn(PETSC_SUCCESS); |
| 351 | } | ||
| 352 | |||
| 353 | /*@ | ||
| 354 | SVDLanczosSetOneSide - Indicate if the variant of the Lanczos method | ||
| 355 | to be used is one-sided or two-sided. | ||
| 356 | |||
| 357 | Logically Collective | ||
| 358 | |||
| 359 | Input Parameters: | ||
| 360 | + svd - the singular value solver context | ||
| 361 | - oneside - boolean flag indicating if the method is one-sided or not | ||
| 362 | |||
| 363 | Options Database Key: | ||
| 364 | . -svd_lanczos_oneside (true|false) - enable the one-sided variant | ||
| 365 | |||
| 366 | Note: | ||
| 367 | By default, a two-sided variant is selected, which is sometimes slightly | ||
| 368 | more robust. However, the one-sided variant is faster because it avoids | ||
| 369 | the orthogonalization associated to left singular vectors. It also saves | ||
| 370 | the memory required for storing such vectors. See more details in | ||
| 371 | {cite:p}`Her07c,Her08`. | ||
| 372 | |||
| 373 | Level: advanced | ||
| 374 | |||
| 375 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDTRLanczosSetOneSide()` | ||
| 376 | @*/ | ||
| 377 | 24 | PetscErrorCode SVDLanczosSetOneSide(SVD svd,PetscBool oneside) | |
| 378 | { | ||
| 379 |
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24 | PetscFunctionBegin; |
| 380 |
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24 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 381 |
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24 | PetscValidLogicalCollectiveBool(svd,oneside,2); |
| 382 |
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24 | PetscTryMethod(svd,"SVDLanczosSetOneSide_C",(SVD,PetscBool),(svd,oneside)); |
| 383 |
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24 | PetscFunctionReturn(PETSC_SUCCESS); |
| 384 | } | ||
| 385 | |||
| 386 | 20 | static PetscErrorCode SVDLanczosGetOneSide_Lanczos(SVD svd,PetscBool *oneside) | |
| 387 | { | ||
| 388 | 20 | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 389 | |||
| 390 |
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20 | PetscFunctionBegin; |
| 391 | 20 | *oneside = lanczos->oneside; | |
| 392 |
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20 | PetscFunctionReturn(PETSC_SUCCESS); |
| 393 | } | ||
| 394 | |||
| 395 | /*@ | ||
| 396 | SVDLanczosGetOneSide - Gets if the variant of the Lanczos method | ||
| 397 | to be used is one-sided or two-sided. | ||
| 398 | |||
| 399 | Not Collective | ||
| 400 | |||
| 401 | Input Parameter: | ||
| 402 | . svd - the singular value solver context | ||
| 403 | |||
| 404 | Output Parameter: | ||
| 405 | . oneside - boolean flag indicating if the method is one-sided or not | ||
| 406 | |||
| 407 | Level: advanced | ||
| 408 | |||
| 409 | .seealso: [](ch:svd), `SVDTRLANCZOS`, `SVDLanczosSetOneSide()` | ||
| 410 | @*/ | ||
| 411 | 20 | PetscErrorCode SVDLanczosGetOneSide(SVD svd,PetscBool *oneside) | |
| 412 | { | ||
| 413 |
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20 | PetscFunctionBegin; |
| 414 |
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20 | PetscValidHeaderSpecific(svd,SVD_CLASSID,1); |
| 415 |
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20 | PetscAssertPointer(oneside,2); |
| 416 |
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20 | PetscUseMethod(svd,"SVDLanczosGetOneSide_C",(SVD,PetscBool*),(svd,oneside)); |
| 417 |
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20 | PetscFunctionReturn(PETSC_SUCCESS); |
| 418 | } | ||
| 419 | |||
| 420 | 164 | static PetscErrorCode SVDDestroy_Lanczos(SVD svd) | |
| 421 | { | ||
| 422 |
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164 | PetscFunctionBegin; |
| 423 |
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164 | PetscCall(PetscFree(svd->data)); |
| 424 |
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164 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",NULL)); |
| 425 |
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164 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",NULL)); |
| 426 |
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30 | PetscFunctionReturn(PETSC_SUCCESS); |
| 427 | } | ||
| 428 | |||
| 429 | ✗ | static PetscErrorCode SVDView_Lanczos(SVD svd,PetscViewer viewer) | |
| 430 | { | ||
| 431 | ✗ | SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data; | |
| 432 | ✗ | PetscBool isascii; | |
| 433 | |||
| 434 | ✗ | PetscFunctionBegin; | |
| 435 | ✗ | PetscCall(PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii)); | |
| 436 | ✗ | if (isascii) PetscCall(PetscViewerASCIIPrintf(viewer," %s-sided reorthogonalization\n",lanczos->oneside? "one": "two")); | |
| 437 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 438 | } | ||
| 439 | |||
| 440 | /*MC | ||
| 441 | SVDLANCZOS - SVDLANCZOS = "lanczos" - A basic Golub-Kahan-Lanczos | ||
| 442 | bidiagonalization method with explicit restart. | ||
| 443 | |||
| 444 | Notes: | ||
| 445 | Only available for standard SVD problems. | ||
| 446 | |||
| 447 | This solver is very basic and is not recommended in general, since it | ||
| 448 | will not be competitive with respect to other solvers. | ||
| 449 | |||
| 450 | The implemented method is Lanczos bidiagonalization with explicit restart | ||
| 451 | and deflation. Generally, it is much better to use Lanczos with implicit | ||
| 452 | restart (also known as thick-restart Lanczos) as implemented in `SVDTRLANCZOS`. | ||
| 453 | |||
| 454 | The implementation includes a one-sided orthogonalization option, and | ||
| 455 | efficient parallel orthogonalization, see the details in {cite:p}`Her07c,Her08`. | ||
| 456 | |||
| 457 | Level: beginner | ||
| 458 | |||
| 459 | .seealso: [](ch:svd), [](#sec:svdback), `SVD`, `SVDType`, `SVDSetType()`, `SVDSetProblemType()`, `SVDTRLANCZOS` | ||
| 460 | M*/ | ||
| 461 | 164 | SLEPC_EXTERN PetscErrorCode SVDCreate_Lanczos(SVD svd) | |
| 462 | { | ||
| 463 | 164 | SVD_LANCZOS *ctx; | |
| 464 | |||
| 465 |
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164 | PetscFunctionBegin; |
| 466 |
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164 | PetscCall(PetscNew(&ctx)); |
| 467 | 164 | svd->data = (void*)ctx; | |
| 468 | |||
| 469 | 164 | svd->ops->setup = SVDSetUp_Lanczos; | |
| 470 | 164 | svd->ops->solve = SVDSolve_Lanczos; | |
| 471 | 164 | svd->ops->destroy = SVDDestroy_Lanczos; | |
| 472 | 164 | svd->ops->setfromoptions = SVDSetFromOptions_Lanczos; | |
| 473 | 164 | svd->ops->view = SVDView_Lanczos; | |
| 474 | 164 | svd->ops->computevectors = SVDComputeVectors_Left; | |
| 475 |
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164 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",SVDLanczosSetOneSide_Lanczos)); |
| 476 |
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164 | PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",SVDLanczosGetOneSide_Lanczos)); |
| 477 |
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30 | PetscFunctionReturn(PETSC_SUCCESS); |
| 478 | } | ||
| 479 |