| 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 | BV operations, except those involving global communication | ||
| 12 | */ | ||
| 13 | |||
| 14 | #include <slepc/private/bvimpl.h> /*I "slepcbv.h" I*/ | ||
| 15 | #include <slepcds.h> | ||
| 16 | |||
| 17 | /*@ | ||
| 18 | BVMult - Computes Y = beta*Y + alpha*X*Q. | ||
| 19 | |||
| 20 | Logically Collective | ||
| 21 | |||
| 22 | Input Parameters: | ||
| 23 | + Y - first basis vectors context (modified on output) | ||
| 24 | . alpha - first scalar | ||
| 25 | . beta - second scalar | ||
| 26 | . X - second basis vectors context | ||
| 27 | - Q - (optional) sequential dense matrix | ||
| 28 | |||
| 29 | Notes: | ||
| 30 | X and Y must be different objects. The case X=Y can be addressed with | ||
| 31 | BVMultInPlace(). | ||
| 32 | |||
| 33 | If matrix Q is NULL, then an AXPY operation Y = beta*Y + alpha*X is done | ||
| 34 | (i.e. results as if Q = identity). If provided, | ||
| 35 | the matrix Q must be a sequential dense Mat, with all entries equal on | ||
| 36 | all processes (otherwise each process will compute a different update). | ||
| 37 | The dimensions of Q must be at least m,n where m is the number of active | ||
| 38 | columns of X and n is the number of active columns of Y. | ||
| 39 | |||
| 40 | The leading columns of Y are not modified. Also, if X has leading | ||
| 41 | columns specified, then these columns do not participate in the computation. | ||
| 42 | Hence, only rows (resp. columns) of Q starting from lx (resp. ly) are used, | ||
| 43 | where lx (resp. ly) is the number of leading columns of X (resp. Y). | ||
| 44 | |||
| 45 | Level: intermediate | ||
| 46 | |||
| 47 | .seealso: BVMultVec(), BVMultColumn(), BVMultInPlace(), BVSetActiveColumns() | ||
| 48 | @*/ | ||
| 49 | 69259 | PetscErrorCode BVMult(BV Y,PetscScalar alpha,PetscScalar beta,BV X,Mat Q) | |
| 50 | { | ||
| 51 | 69259 | PetscInt m,n; | |
| 52 | |||
| 53 |
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69259 | PetscFunctionBegin; |
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69259 | PetscValidHeaderSpecific(Y,BV_CLASSID,1); |
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69259 | PetscValidLogicalCollectiveScalar(Y,alpha,2); |
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69259 | PetscValidLogicalCollectiveScalar(Y,beta,3); |
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69259 | PetscValidHeaderSpecific(X,BV_CLASSID,4); |
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69259 | if (Q) PetscValidHeaderSpecific(Q,MAT_CLASSID,5); |
| 59 |
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69259 | PetscValidType(Y,1); |
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69259 | BVCheckSizes(Y,1); |
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69259 | BVCheckOp(Y,1,mult); |
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69259 | PetscValidType(X,4); |
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69259 | BVCheckSizes(X,4); |
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69259 | if (Q) PetscValidType(Q,5); |
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69259 | PetscCheckSameTypeAndComm(Y,1,X,4); |
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69259 | PetscCheck(X!=Y,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_WRONG,"X and Y arguments must be different"); |
| 67 |
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69259 | if (Q) { |
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56214 | SlepcMatCheckSeq(Q); |
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56214 | PetscCall(MatGetSize(Q,&m,&n)); |
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56214 | PetscCheck(m>=X->k,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_SIZ,"Mat argument has %" PetscInt_FMT " rows, should have at least %" PetscInt_FMT,m,X->k); |
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56214 | PetscCheck(n>=Y->k,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_SIZ,"Mat argument has %" PetscInt_FMT " columns, should have at least %" PetscInt_FMT,n,Y->k); |
| 72 | } | ||
| 73 |
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69259 | PetscCheck(X->n==Y->n,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching local dimension X %" PetscInt_FMT ", Y %" PetscInt_FMT,X->n,Y->n); |
| 74 | |||
| 75 |
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69259 | PetscCall(PetscLogEventBegin(BV_Mult,X,Y,0,0)); |
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69259 | PetscUseTypeMethod(Y,mult,alpha,beta,X,Q); |
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69259 | PetscCall(PetscLogEventEnd(BV_Mult,X,Y,0,0)); |
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69259 | PetscCall(PetscObjectStateIncrease((PetscObject)Y)); |
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69259 | PetscFunctionReturn(PETSC_SUCCESS); |
| 80 | } | ||
| 81 | |||
| 82 | /*@ | ||
| 83 | BVMultVec - Computes y = beta*y + alpha*X*q. | ||
| 84 | |||
| 85 | Logically Collective | ||
| 86 | |||
| 87 | Input Parameters: | ||
| 88 | + X - a basis vectors object | ||
| 89 | . alpha - first scalar | ||
| 90 | . beta - second scalar | ||
| 91 | . y - a vector (modified on output) | ||
| 92 | - q - an array of scalars | ||
| 93 | |||
| 94 | Notes: | ||
| 95 | This operation is the analogue of BVMult() but with a BV and a Vec, | ||
| 96 | instead of two BV. Note that arguments are listed in different order | ||
| 97 | with respect to BVMult(). | ||
| 98 | |||
| 99 | If X has leading columns specified, then these columns do not participate | ||
| 100 | in the computation. | ||
| 101 | |||
| 102 | The length of array q must be equal to the number of active columns of X | ||
| 103 | minus the number of leading columns, i.e. the first entry of q multiplies | ||
| 104 | the first non-leading column. | ||
| 105 | |||
| 106 | Level: intermediate | ||
| 107 | |||
| 108 | .seealso: BVMult(), BVMultColumn(), BVMultInPlace(), BVSetActiveColumns() | ||
| 109 | @*/ | ||
| 110 | 3705446 | PetscErrorCode BVMultVec(BV X,PetscScalar alpha,PetscScalar beta,Vec y,PetscScalar q[]) | |
| 111 | { | ||
| 112 | 3705446 | PetscInt n,N; | |
| 113 | |||
| 114 |
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3705446 | PetscFunctionBegin; |
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3705446 | PetscValidHeaderSpecific(X,BV_CLASSID,1); |
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3705446 | PetscValidLogicalCollectiveScalar(X,alpha,2); |
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3705446 | PetscValidLogicalCollectiveScalar(X,beta,3); |
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3705446 | PetscValidHeaderSpecific(y,VEC_CLASSID,4); |
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3705446 | PetscAssertPointer(q,5); |
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3705446 | PetscValidType(X,1); |
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3705446 | BVCheckSizes(X,1); |
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3705446 | BVCheckOp(X,1,multvec); |
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3705446 | PetscValidType(y,4); |
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3705446 | PetscCheckSameComm(X,1,y,4); |
| 125 | |||
| 126 |
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3705446 | PetscCall(VecGetSize(y,&N)); |
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3705446 | PetscCall(VecGetLocalSize(y,&n)); |
| 128 |
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3705446 | PetscCheck(N==X->N && n==X->n,PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_INCOMP,"Vec sizes (global %" PetscInt_FMT ", local %" PetscInt_FMT ") do not match BV sizes (global %" PetscInt_FMT ", local %" PetscInt_FMT ")",N,n,X->N,X->n); |
| 129 | |||
| 130 |
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3705446 | PetscCall(PetscLogEventBegin(BV_MultVec,X,y,0,0)); |
| 131 |
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3705446 | PetscUseTypeMethod(X,multvec,alpha,beta,y,q); |
| 132 |
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3705446 | PetscCall(PetscLogEventEnd(BV_MultVec,X,y,0,0)); |
| 133 |
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742489 | PetscFunctionReturn(PETSC_SUCCESS); |
| 134 | } | ||
| 135 | |||
| 136 | /*@ | ||
| 137 | BVMultColumn - Computes y = beta*y + alpha*X*q, where y is the j-th column | ||
| 138 | of X. | ||
| 139 | |||
| 140 | Logically Collective | ||
| 141 | |||
| 142 | Input Parameters: | ||
| 143 | + X - a basis vectors object | ||
| 144 | . alpha - first scalar | ||
| 145 | . beta - second scalar | ||
| 146 | . j - the column index | ||
| 147 | - q - an array of scalars | ||
| 148 | |||
| 149 | Notes: | ||
| 150 | This operation is equivalent to BVMultVec() but it uses column j of X | ||
| 151 | rather than taking a Vec as an argument. The number of active columns of | ||
| 152 | X is set to j before the computation, and restored afterwards. | ||
| 153 | If X has leading columns specified, then these columns do not participate | ||
| 154 | in the computation. Therefore, the length of array q must be equal to j | ||
| 155 | minus the number of leading columns. | ||
| 156 | |||
| 157 | Developer Notes: | ||
| 158 | If q is NULL, then the coefficients are taken from position nc+l of the | ||
| 159 | internal buffer vector, see BVGetBufferVec(). | ||
| 160 | |||
| 161 | Level: advanced | ||
| 162 | |||
| 163 | .seealso: BVMult(), BVMultVec(), BVMultInPlace(), BVSetActiveColumns() | ||
| 164 | @*/ | ||
| 165 | 3022658 | PetscErrorCode BVMultColumn(BV X,PetscScalar alpha,PetscScalar beta,PetscInt j,PetscScalar *q) | |
| 166 | { | ||
| 167 | 3022658 | PetscInt ksave; | |
| 168 | 3022658 | Vec y; | |
| 169 | |||
| 170 |
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3022658 | PetscFunctionBegin; |
| 171 |
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3022658 | PetscValidHeaderSpecific(X,BV_CLASSID,1); |
| 172 |
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3022658 | PetscValidLogicalCollectiveScalar(X,alpha,2); |
| 173 |
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3022658 | PetscValidLogicalCollectiveScalar(X,beta,3); |
| 174 |
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3022658 | PetscValidLogicalCollectiveInt(X,j,4); |
| 175 |
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3022658 | PetscValidType(X,1); |
| 176 |
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3022658 | BVCheckSizes(X,1); |
| 177 | |||
| 178 |
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3022658 | PetscCheck(j>=0,PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_OUTOFRANGE,"Index j must be non-negative"); |
| 179 |
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3022658 | PetscCheck(j<X->m,PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_OUTOFRANGE,"Index j=%" PetscInt_FMT " but BV only has %" PetscInt_FMT " columns",j,X->m); |
| 180 | |||
| 181 |
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3022658 | PetscCall(PetscLogEventBegin(BV_MultVec,X,0,0,0)); |
| 182 | 3022658 | ksave = X->k; | |
| 183 | 3022658 | X->k = j; | |
| 184 |
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3022658 | if (!q && !X->buffer) PetscCall(BVGetBufferVec(X,&X->buffer)); |
| 185 |
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3022658 | PetscCall(BVGetColumn(X,j,&y)); |
| 186 |
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3022658 | PetscUseTypeMethod(X,multvec,alpha,beta,y,q); |
| 187 |
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3022658 | PetscCall(BVRestoreColumn(X,j,&y)); |
| 188 | 3022658 | X->k = ksave; | |
| 189 |
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3022658 | PetscCall(PetscLogEventEnd(BV_MultVec,X,0,0,0)); |
| 190 |
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3022658 | PetscCall(PetscObjectStateIncrease((PetscObject)X)); |
| 191 |
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3022658 | PetscFunctionReturn(PETSC_SUCCESS); |
| 192 | } | ||
| 193 | |||
| 194 | /*@ | ||
| 195 | BVMultInPlace - Update a set of vectors as V(:,s:e-1) = V*Q(:,s:e-1). | ||
| 196 | |||
| 197 | Logically Collective | ||
| 198 | |||
| 199 | Input Parameters: | ||
| 200 | + Q - a sequential dense matrix | ||
| 201 | . s - first column of V to be overwritten | ||
| 202 | - e - first column of V not to be overwritten | ||
| 203 | |||
| 204 | Input/Output Parameter: | ||
| 205 | . V - basis vectors | ||
| 206 | |||
| 207 | Notes: | ||
| 208 | The matrix Q must be a sequential dense Mat, with all entries equal on | ||
| 209 | all processes (otherwise each process will compute a different update). | ||
| 210 | |||
| 211 | This function computes V(:,s:e-1) = V*Q(:,s:e-1), that is, given a set of | ||
| 212 | vectors V, columns from s to e-1 are overwritten with columns from s to | ||
| 213 | e-1 of the matrix-matrix product V*Q. Only columns s to e-1 of Q are | ||
| 214 | referenced. | ||
| 215 | |||
| 216 | Level: intermediate | ||
| 217 | |||
| 218 | .seealso: BVMult(), BVMultVec(), BVMultInPlaceHermitianTranspose(), BVSetActiveColumns() | ||
| 219 | @*/ | ||
| 220 | 305598 | PetscErrorCode BVMultInPlace(BV V,Mat Q,PetscInt s,PetscInt e) | |
| 221 | { | ||
| 222 | 305598 | PetscInt m,n; | |
| 223 | |||
| 224 |
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305598 | PetscFunctionBegin; |
| 225 |
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305598 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 226 |
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305598 | PetscValidHeaderSpecific(Q,MAT_CLASSID,2); |
| 227 |
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305598 | PetscValidLogicalCollectiveInt(V,s,3); |
| 228 |
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305598 | PetscValidLogicalCollectiveInt(V,e,4); |
| 229 |
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305598 | PetscValidType(V,1); |
| 230 |
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305598 | BVCheckSizes(V,1); |
| 231 |
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305598 | PetscValidType(Q,2); |
| 232 |
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305598 | SlepcMatCheckSeq(Q); |
| 233 | |||
| 234 |
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305598 | PetscCheck(s>=V->l && s<=V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Argument s has wrong value %" PetscInt_FMT ", should be between %" PetscInt_FMT " and %" PetscInt_FMT,s,V->l,V->m); |
| 235 |
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305598 | PetscCheck(e>=V->l && e<=V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Argument e has wrong value %" PetscInt_FMT ", should be between %" PetscInt_FMT " and %" PetscInt_FMT,e,V->l,V->m); |
| 236 |
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305598 | PetscCall(MatGetSize(Q,&m,&n)); |
| 237 |
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305598 | PetscCheck(m>=V->k,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Mat argument has %" PetscInt_FMT " rows, should have at least %" PetscInt_FMT,m,V->k); |
| 238 |
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305598 | PetscCheck(e<=n,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Mat argument only has %" PetscInt_FMT " columns, the requested value of e is larger: %" PetscInt_FMT,n,e); |
| 239 | |||
| 240 |
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305598 | PetscCall(PetscLogEventBegin(BV_MultInPlace,V,Q,0,0)); |
| 241 |
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305598 | PetscUseTypeMethod(V,multinplace,Q,s,e); |
| 242 |
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305598 | PetscCall(PetscLogEventEnd(BV_MultInPlace,V,Q,0,0)); |
| 243 |
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305598 | PetscCall(PetscObjectStateIncrease((PetscObject)V)); |
| 244 |
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305598 | PetscFunctionReturn(PETSC_SUCCESS); |
| 245 | } | ||
| 246 | |||
| 247 | /*@ | ||
| 248 | BVMultInPlaceHermitianTranspose - Update a set of vectors as V(:,s:e-1) = V*Q'(:,s:e-1). | ||
| 249 | |||
| 250 | Logically Collective | ||
| 251 | |||
| 252 | Input Parameters: | ||
| 253 | + Q - a sequential dense matrix | ||
| 254 | . s - first column of V to be overwritten | ||
| 255 | - e - first column of V not to be overwritten | ||
| 256 | |||
| 257 | Input/Output Parameter: | ||
| 258 | . V - basis vectors | ||
| 259 | |||
| 260 | Notes: | ||
| 261 | This is a variant of BVMultInPlace() where the conjugate transpose | ||
| 262 | of Q is used. | ||
| 263 | |||
| 264 | Level: intermediate | ||
| 265 | |||
| 266 | .seealso: BVMultInPlace() | ||
| 267 | @*/ | ||
| 268 | 40 | PetscErrorCode BVMultInPlaceHermitianTranspose(BV V,Mat Q,PetscInt s,PetscInt e) | |
| 269 | { | ||
| 270 | 40 | PetscInt m,n; | |
| 271 | |||
| 272 |
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40 | PetscFunctionBegin; |
| 273 |
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40 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 274 |
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40 | PetscValidHeaderSpecific(Q,MAT_CLASSID,2); |
| 275 |
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40 | PetscValidLogicalCollectiveInt(V,s,3); |
| 276 |
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40 | PetscValidLogicalCollectiveInt(V,e,4); |
| 277 |
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40 | PetscValidType(V,1); |
| 278 |
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40 | BVCheckSizes(V,1); |
| 279 |
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40 | PetscValidType(Q,2); |
| 280 |
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40 | SlepcMatCheckSeq(Q); |
| 281 | |||
| 282 |
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40 | PetscCheck(s>=V->l && s<=V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Argument s has wrong value %" PetscInt_FMT ", should be between %" PetscInt_FMT " and %" PetscInt_FMT,s,V->l,V->m); |
| 283 |
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40 | PetscCheck(e>=V->l && e<=V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Argument e has wrong value %" PetscInt_FMT ", should be between %" PetscInt_FMT " and %" PetscInt_FMT,e,V->l,V->m); |
| 284 |
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40 | PetscCall(MatGetSize(Q,&m,&n)); |
| 285 |
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40 | PetscCheck(n>=V->k,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Mat argument has %" PetscInt_FMT " columns, should have at least %" PetscInt_FMT,n,V->k); |
| 286 |
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40 | PetscCheck(e<=m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Mat argument only has %" PetscInt_FMT " rows, the requested value of e is larger: %" PetscInt_FMT,m,e); |
| 287 | |||
| 288 |
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40 | PetscCall(PetscLogEventBegin(BV_MultInPlace,V,Q,0,0)); |
| 289 |
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40 | PetscUseTypeMethod(V,multinplacetrans,Q,s,e); |
| 290 |
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40 | PetscCall(PetscLogEventEnd(BV_MultInPlace,V,Q,0,0)); |
| 291 |
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40 | PetscCall(PetscObjectStateIncrease((PetscObject)V)); |
| 292 |
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40 | PetscFunctionReturn(PETSC_SUCCESS); |
| 293 | } | ||
| 294 | |||
| 295 | /*@ | ||
| 296 | BVScale - Multiply the BV entries by a scalar value. | ||
| 297 | |||
| 298 | Logically Collective | ||
| 299 | |||
| 300 | Input Parameters: | ||
| 301 | + bv - basis vectors | ||
| 302 | - alpha - scaling factor | ||
| 303 | |||
| 304 | Note: | ||
| 305 | All active columns (except the leading ones) are scaled. | ||
| 306 | |||
| 307 | Level: intermediate | ||
| 308 | |||
| 309 | .seealso: BVScaleColumn(), BVSetActiveColumns() | ||
| 310 | @*/ | ||
| 311 | 14353 | PetscErrorCode BVScale(BV bv,PetscScalar alpha) | |
| 312 | { | ||
| 313 |
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14353 | PetscFunctionBegin; |
| 314 |
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14353 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 315 |
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14353 | PetscValidLogicalCollectiveScalar(bv,alpha,2); |
| 316 |
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14353 | PetscValidType(bv,1); |
| 317 |
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14353 | BVCheckSizes(bv,1); |
| 318 |
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14353 | if (alpha == (PetscScalar)1.0) PetscFunctionReturn(PETSC_SUCCESS); |
| 319 | |||
| 320 |
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3617 | PetscCall(PetscLogEventBegin(BV_Scale,bv,0,0,0)); |
| 321 |
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3617 | PetscUseTypeMethod(bv,scale,-1,alpha); |
| 322 |
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3617 | PetscCall(PetscLogEventEnd(BV_Scale,bv,0,0,0)); |
| 323 |
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3617 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 324 |
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3617 | PetscFunctionReturn(PETSC_SUCCESS); |
| 325 | } | ||
| 326 | |||
| 327 | /*@ | ||
| 328 | BVScaleColumn - Scale one column of a BV. | ||
| 329 | |||
| 330 | Logically Collective | ||
| 331 | |||
| 332 | Input Parameters: | ||
| 333 | + bv - basis vectors | ||
| 334 | . j - column number to be scaled | ||
| 335 | - alpha - scaling factor | ||
| 336 | |||
| 337 | Level: intermediate | ||
| 338 | |||
| 339 | .seealso: BVScale(), BVSetActiveColumns() | ||
| 340 | @*/ | ||
| 341 | 1042494 | PetscErrorCode BVScaleColumn(BV bv,PetscInt j,PetscScalar alpha) | |
| 342 | { | ||
| 343 |
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1042494 | PetscFunctionBegin; |
| 344 |
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1042494 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 345 |
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1042494 | PetscValidLogicalCollectiveInt(bv,j,2); |
| 346 |
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1042494 | PetscValidLogicalCollectiveScalar(bv,alpha,3); |
| 347 |
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1042494 | PetscValidType(bv,1); |
| 348 |
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1042494 | BVCheckSizes(bv,1); |
| 349 | |||
| 350 |
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1042494 | PetscCheck(j>=0 && j<bv->m,PetscObjectComm((PetscObject)bv),PETSC_ERR_ARG_OUTOFRANGE,"Argument j has wrong value %" PetscInt_FMT ", the number of columns is %" PetscInt_FMT,j,bv->m); |
| 351 |
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1042494 | if (alpha == (PetscScalar)1.0) PetscFunctionReturn(PETSC_SUCCESS); |
| 352 | |||
| 353 |
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1028529 | PetscCall(PetscLogEventBegin(BV_Scale,bv,0,0,0)); |
| 354 |
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1028529 | PetscUseTypeMethod(bv,scale,j,alpha); |
| 355 |
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1028529 | PetscCall(PetscLogEventEnd(BV_Scale,bv,0,0,0)); |
| 356 |
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1028529 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 357 |
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1028529 | PetscFunctionReturn(PETSC_SUCCESS); |
| 358 | } | ||
| 359 | |||
| 360 | 30629 | static inline PetscErrorCode BVSetRandomColumn_Private(BV bv,PetscInt k) | |
| 361 | { | ||
| 362 | 30629 | PetscInt i,low,high; | |
| 363 | 30629 | PetscScalar *px,t; | |
| 364 | 30629 | Vec x; | |
| 365 | |||
| 366 |
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30629 | PetscFunctionBegin; |
| 367 |
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30629 | PetscCall(BVGetColumn(bv,k,&x)); |
| 368 |
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30629 | if (bv->rrandom) { /* generate the same vector irrespective of number of processes */ |
| 369 |
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400 | PetscCall(VecGetOwnershipRange(x,&low,&high)); |
| 370 |
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400 | PetscCall(VecGetArray(x,&px)); |
| 371 |
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25200 | for (i=0;i<bv->N;i++) { |
| 372 |
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24800 | PetscCall(PetscRandomGetValue(bv->rand,&t)); |
| 373 |
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24800 | if (i>=low && i<high) px[i-low] = t; |
| 374 | } | ||
| 375 |
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400 | PetscCall(VecRestoreArray(x,&px)); |
| 376 |
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30229 | } else PetscCall(VecSetRandom(x,bv->rand)); |
| 377 |
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30629 | PetscCall(BVRestoreColumn(bv,k,&x)); |
| 378 |
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5932 | PetscFunctionReturn(PETSC_SUCCESS); |
| 379 | } | ||
| 380 | |||
| 381 | 1148 | static inline PetscErrorCode BVSetRandomNormalColumn_Private(BV bv,PetscInt k,Vec w1,Vec w2) | |
| 382 | { | ||
| 383 | 1148 | PetscInt i,low,high; | |
| 384 | 1148 | PetscScalar *px,s,t; | |
| 385 | 1148 | Vec x; | |
| 386 | |||
| 387 |
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1148 | PetscFunctionBegin; |
| 388 |
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1148 | PetscCall(BVGetColumn(bv,k,&x)); |
| 389 |
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1148 | if (bv->rrandom) { /* generate the same vector irrespective of number of processes */ |
| 390 | ✗ | PetscCall(VecGetOwnershipRange(x,&low,&high)); | |
| 391 | ✗ | PetscCall(VecGetArray(x,&px)); | |
| 392 | ✗ | for (i=0;i<bv->N;i++) { | |
| 393 | ✗ | PetscCall(PetscRandomGetValue(bv->rand,&s)); | |
| 394 | ✗ | PetscCall(PetscRandomGetValue(bv->rand,&t)); | |
| 395 | ✗ | if (i>=low && i<high) { | |
| 396 | #if defined(PETSC_USE_COMPLEX) | ||
| 397 | ✗ | px[i-low] = PetscCMPLX(PetscSqrtReal(-2.0*PetscLogReal(PetscRealPart(s)))*PetscCosReal(2.0*PETSC_PI*PetscRealPart(t)),PetscSqrtReal(-2.0*PetscLogReal(PetscImaginaryPart(s)))*PetscCosReal(2.0*PETSC_PI*PetscImaginaryPart(t))); | |
| 398 | #else | ||
| 399 | ✗ | px[i-low] = PetscSqrtReal(-2.0*PetscLogReal(s))*PetscCosReal(2.0*PETSC_PI*t); | |
| 400 | #endif | ||
| 401 | } | ||
| 402 | } | ||
| 403 | ✗ | PetscCall(VecRestoreArray(x,&px)); | |
| 404 |
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1148 | } else PetscCall(VecSetRandomNormal(x,bv->rand,w1,w2)); |
| 405 |
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1148 | PetscCall(BVRestoreColumn(bv,k,&x)); |
| 406 |
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224 | PetscFunctionReturn(PETSC_SUCCESS); |
| 407 | } | ||
| 408 | |||
| 409 | 6924 | static inline PetscErrorCode BVSetRandomSignColumn_Private(BV bv,PetscInt k) | |
| 410 | { | ||
| 411 | 6924 | PetscInt i,low,high; | |
| 412 | 6924 | PetscScalar *px,t; | |
| 413 | 6924 | Vec x; | |
| 414 | |||
| 415 |
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6924 | PetscFunctionBegin; |
| 416 |
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6924 | PetscCall(BVGetColumn(bv,k,&x)); |
| 417 |
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6924 | PetscCall(VecGetOwnershipRange(x,&low,&high)); |
| 418 |
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6924 | if (bv->rrandom) { /* generate the same vector irrespective of number of processes */ |
| 419 | ✗ | PetscCall(VecGetArray(x,&px)); | |
| 420 | ✗ | for (i=0;i<bv->N;i++) { | |
| 421 | ✗ | PetscCall(PetscRandomGetValue(bv->rand,&t)); | |
| 422 | ✗ | if (i>=low && i<high) px[i-low] = (PetscRealPart(t)<0.5)? -1.0: 1.0; | |
| 423 | } | ||
| 424 | ✗ | PetscCall(VecRestoreArray(x,&px)); | |
| 425 | } else { | ||
| 426 |
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6924 | PetscCall(VecSetRandom(x,bv->rand)); |
| 427 |
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6924 | PetscCall(VecGetArray(x,&px)); |
| 428 |
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1511734 | for (i=low;i<high;i++) { |
| 429 |
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2239352 | px[i-low] = (PetscRealPart(px[i-low])<0.5)? -1.0: 1.0; |
| 430 | } | ||
| 431 |
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6924 | PetscCall(VecRestoreArray(x,&px)); |
| 432 | } | ||
| 433 |
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6924 | PetscCall(BVRestoreColumn(bv,k,&x)); |
| 434 |
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1462 | PetscFunctionReturn(PETSC_SUCCESS); |
| 435 | } | ||
| 436 | |||
| 437 | /*@ | ||
| 438 | BVSetRandom - Set the columns of a BV to random numbers. | ||
| 439 | |||
| 440 | Logically Collective | ||
| 441 | |||
| 442 | Input Parameters: | ||
| 443 | . bv - basis vectors | ||
| 444 | |||
| 445 | Note: | ||
| 446 | All active columns (except the leading ones) are modified. | ||
| 447 | |||
| 448 | Level: advanced | ||
| 449 | |||
| 450 | .seealso: BVSetRandomContext(), BVSetRandomColumn(), BVSetRandomNormal(), BVSetRandomSign(), BVSetRandomCond(), BVSetActiveColumns() | ||
| 451 | @*/ | ||
| 452 | 80 | PetscErrorCode BVSetRandom(BV bv) | |
| 453 | { | ||
| 454 | 80 | PetscInt k; | |
| 455 | |||
| 456 |
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80 | PetscFunctionBegin; |
| 457 |
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80 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 458 |
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80 | PetscValidType(bv,1); |
| 459 |
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80 | BVCheckSizes(bv,1); |
| 460 | |||
| 461 |
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80 | PetscCall(BVGetRandomContext(bv,&bv->rand)); |
| 462 |
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80 | PetscCall(PetscLogEventBegin(BV_SetRandom,bv,0,0,0)); |
| 463 |
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480 | for (k=bv->l;k<bv->k;k++) PetscCall(BVSetRandomColumn_Private(bv,k)); |
| 464 |
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80 | PetscCall(PetscLogEventEnd(BV_SetRandom,bv,0,0,0)); |
| 465 |
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80 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 466 |
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80 | PetscFunctionReturn(PETSC_SUCCESS); |
| 467 | } | ||
| 468 | |||
| 469 | /*@ | ||
| 470 | BVSetRandomColumn - Set one column of a BV to random numbers. | ||
| 471 | |||
| 472 | Logically Collective | ||
| 473 | |||
| 474 | Input Parameters: | ||
| 475 | + bv - basis vectors | ||
| 476 | - j - column number to be set | ||
| 477 | |||
| 478 | Level: advanced | ||
| 479 | |||
| 480 | .seealso: BVSetRandomContext(), BVSetRandom(), BVSetRandomNormal(), BVSetRandomCond() | ||
| 481 | @*/ | ||
| 482 | 29909 | PetscErrorCode BVSetRandomColumn(BV bv,PetscInt j) | |
| 483 | { | ||
| 484 |
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29909 | PetscFunctionBegin; |
| 485 |
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29909 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 486 |
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29909 | PetscValidLogicalCollectiveInt(bv,j,2); |
| 487 |
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29909 | PetscValidType(bv,1); |
| 488 |
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29909 | BVCheckSizes(bv,1); |
| 489 |
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29909 | PetscCheck(j>=0 && j<bv->m,PetscObjectComm((PetscObject)bv),PETSC_ERR_ARG_OUTOFRANGE,"Argument j has wrong value %" PetscInt_FMT ", the number of columns is %" PetscInt_FMT,j,bv->m); |
| 490 | |||
| 491 |
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29909 | PetscCall(BVGetRandomContext(bv,&bv->rand)); |
| 492 |
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29909 | PetscCall(PetscLogEventBegin(BV_SetRandom,bv,0,0,0)); |
| 493 |
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29909 | PetscCall(BVSetRandomColumn_Private(bv,j)); |
| 494 |
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29909 | PetscCall(PetscLogEventEnd(BV_SetRandom,bv,0,0,0)); |
| 495 |
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29909 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 496 |
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29909 | PetscFunctionReturn(PETSC_SUCCESS); |
| 497 | } | ||
| 498 | |||
| 499 | /*@ | ||
| 500 | BVSetRandomNormal - Set the columns of a BV to random numbers with a normal | ||
| 501 | distribution. | ||
| 502 | |||
| 503 | Logically Collective | ||
| 504 | |||
| 505 | Input Parameter: | ||
| 506 | . bv - basis vectors | ||
| 507 | |||
| 508 | Notes: | ||
| 509 | All active columns (except the leading ones) are modified. | ||
| 510 | |||
| 511 | Other functions such as BVSetRandom(), BVSetRandomColumn(), and BVSetRandomCond() | ||
| 512 | produce random numbers with a uniform distribution. This function returns values | ||
| 513 | that fit a normal distribution (Gaussian). | ||
| 514 | |||
| 515 | Developer Notes: | ||
| 516 | The current implementation obtains each of the columns by applying the Box-Muller | ||
| 517 | transform on two random vectors with uniformly distributed entries. | ||
| 518 | |||
| 519 | Level: advanced | ||
| 520 | |||
| 521 | .seealso: BVSetRandomContext(), BVSetRandom(), BVSetRandomColumn(), BVSetRandomCond(), BVSetActiveColumns() | ||
| 522 | @*/ | ||
| 523 | 109 | PetscErrorCode BVSetRandomNormal(BV bv) | |
| 524 | { | ||
| 525 | 109 | PetscInt k; | |
| 526 | 109 | Vec w1=NULL,w2=NULL; | |
| 527 | |||
| 528 |
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109 | PetscFunctionBegin; |
| 529 |
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109 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 530 |
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109 | PetscValidType(bv,1); |
| 531 |
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109 | BVCheckSizes(bv,1); |
| 532 | |||
| 533 |
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109 | PetscCall(BVGetRandomContext(bv,&bv->rand)); |
| 534 |
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109 | if (!bv->rrandom) { |
| 535 |
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109 | PetscCall(BVCreateVec(bv,&w1)); |
| 536 |
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109 | PetscCall(BVCreateVec(bv,&w2)); |
| 537 | } | ||
| 538 |
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109 | PetscCall(PetscLogEventBegin(BV_SetRandom,bv,0,0,0)); |
| 539 |
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1257 | for (k=bv->l;k<bv->k;k++) PetscCall(BVSetRandomNormalColumn_Private(bv,k,w1,w2)); |
| 540 |
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109 | PetscCall(PetscLogEventEnd(BV_SetRandom,bv,0,0,0)); |
| 541 |
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109 | if (!bv->rrandom) { |
| 542 |
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109 | PetscCall(VecDestroy(&w1)); |
| 543 |
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109 | PetscCall(VecDestroy(&w2)); |
| 544 | } | ||
| 545 |
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109 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 546 |
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109 | PetscFunctionReturn(PETSC_SUCCESS); |
| 547 | } | ||
| 548 | |||
| 549 | /*@ | ||
| 550 | BVSetRandomSign - Set the entries of a BV to values 1 or -1 with equal | ||
| 551 | probability. | ||
| 552 | |||
| 553 | Logically Collective | ||
| 554 | |||
| 555 | Input Parameter: | ||
| 556 | . bv - basis vectors | ||
| 557 | |||
| 558 | Notes: | ||
| 559 | All active columns (except the leading ones) are modified. | ||
| 560 | |||
| 561 | This function is used, e.g., in contour integral methods when estimating | ||
| 562 | the number of eigenvalues enclosed by the contour via an unbiased | ||
| 563 | estimator of tr(f(A)) [Bai et al., JCAM 1996]. | ||
| 564 | |||
| 565 | Developer Notes: | ||
| 566 | The current implementation obtains random numbers and then replaces them | ||
| 567 | with -1 or 1 depending on the value being less than 0.5 or not. | ||
| 568 | |||
| 569 | Level: advanced | ||
| 570 | |||
| 571 | .seealso: BVSetRandomContext(), BVSetRandom(), BVSetRandomColumn(), BVSetActiveColumns() | ||
| 572 | @*/ | ||
| 573 | 572 | PetscErrorCode BVSetRandomSign(BV bv) | |
| 574 | { | ||
| 575 | 572 | PetscScalar low,high; | |
| 576 | 572 | PetscInt k; | |
| 577 | |||
| 578 |
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572 | PetscFunctionBegin; |
| 579 |
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572 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 580 |
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572 | PetscValidType(bv,1); |
| 581 |
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572 | BVCheckSizes(bv,1); |
| 582 | |||
| 583 |
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572 | PetscCall(BVGetRandomContext(bv,&bv->rand)); |
| 584 |
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572 | PetscCall(PetscRandomGetInterval(bv->rand,&low,&high)); |
| 585 |
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572 | PetscCheck(PetscRealPart(low)==0.0 && PetscRealPart(high)==1.0,PetscObjectComm((PetscObject)bv),PETSC_ERR_ARG_WRONGSTATE,"The PetscRandom object in the BV must have interval [0,1]"); |
| 586 |
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572 | PetscCall(PetscLogEventBegin(BV_SetRandom,bv,0,0,0)); |
| 587 |
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7496 | for (k=bv->l;k<bv->k;k++) PetscCall(BVSetRandomSignColumn_Private(bv,k)); |
| 588 |
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572 | PetscCall(PetscLogEventEnd(BV_SetRandom,bv,0,0,0)); |
| 589 |
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572 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 590 |
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572 | PetscFunctionReturn(PETSC_SUCCESS); |
| 591 | } | ||
| 592 | |||
| 593 | /*@ | ||
| 594 | BVSetRandomCond - Set the columns of a BV to random numbers, in a way that | ||
| 595 | the generated matrix has a given condition number. | ||
| 596 | |||
| 597 | Logically Collective | ||
| 598 | |||
| 599 | Input Parameters: | ||
| 600 | + bv - basis vectors | ||
| 601 | - condn - condition number | ||
| 602 | |||
| 603 | Note: | ||
| 604 | All active columns (except the leading ones) are modified. | ||
| 605 | |||
| 606 | Level: advanced | ||
| 607 | |||
| 608 | .seealso: BVSetRandomContext(), BVSetRandom(), BVSetRandomColumn(), BVSetRandomNormal(), BVSetActiveColumns() | ||
| 609 | @*/ | ||
| 610 | 40 | PetscErrorCode BVSetRandomCond(BV bv,PetscReal condn) | |
| 611 | { | ||
| 612 | 40 | PetscInt k,i; | |
| 613 | 40 | PetscScalar *eig,*d; | |
| 614 | 40 | DS ds; | |
| 615 | 40 | Mat A,X,Xt,M,G; | |
| 616 | |||
| 617 |
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40 | PetscFunctionBegin; |
| 618 |
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40 | PetscValidHeaderSpecific(bv,BV_CLASSID,1); |
| 619 |
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40 | PetscValidType(bv,1); |
| 620 |
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40 | BVCheckSizes(bv,1); |
| 621 | |||
| 622 |
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40 | PetscCall(BVGetRandomContext(bv,&bv->rand)); |
| 623 |
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40 | PetscCall(PetscLogEventBegin(BV_SetRandom,bv,0,0,0)); |
| 624 | /* B = rand(n,k) */ | ||
| 625 |
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360 | for (k=bv->l;k<bv->k;k++) PetscCall(BVSetRandomColumn_Private(bv,k)); |
| 626 |
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40 | PetscCall(DSCreate(PetscObjectComm((PetscObject)bv),&ds)); |
| 627 |
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40 | PetscCall(DSSetType(ds,DSHEP)); |
| 628 |
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40 | PetscCall(DSAllocate(ds,bv->m)); |
| 629 |
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40 | PetscCall(DSSetDimensions(ds,bv->k,bv->l,bv->k)); |
| 630 | /* [V,S] = eig(B'*B) */ | ||
| 631 |
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40 | PetscCall(DSGetMat(ds,DS_MAT_A,&A)); |
| 632 |
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40 | PetscCall(BVDot(bv,bv,A)); |
| 633 |
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40 | PetscCall(DSRestoreMat(ds,DS_MAT_A,&A)); |
| 634 |
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40 | PetscCall(PetscMalloc1(bv->m,&eig)); |
| 635 |
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40 | PetscCall(DSSolve(ds,eig,NULL)); |
| 636 |
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40 | PetscCall(DSSynchronize(ds,eig,NULL)); |
| 637 |
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40 | PetscCall(DSVectors(ds,DS_MAT_X,NULL,NULL)); |
| 638 | /* M = diag(linspace(1/condn,1,n)./sqrt(diag(S)))' */ | ||
| 639 |
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40 | PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,bv->k,bv->k,NULL,&M)); |
| 640 |
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40 | PetscCall(MatZeroEntries(M)); |
| 641 |
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40 | PetscCall(MatDenseGetArray(M,&d)); |
| 642 |
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360 | for (i=0;i<bv->k;i++) d[i+i*bv->m] = (1.0/condn+(1.0-1.0/condn)/(bv->k-1)*i)/PetscSqrtScalar(eig[i]); |
| 643 |
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40 | PetscCall(MatDenseRestoreArray(M,&d)); |
| 644 | /* G = X*M*X' */ | ||
| 645 |
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40 | PetscCall(DSGetMat(ds,DS_MAT_X,&X)); |
| 646 |
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40 | PetscCall(MatTranspose(X,MAT_INITIAL_MATRIX,&Xt)); |
| 647 |
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40 | PetscCall(MatProductCreate(Xt,M,NULL,&G)); |
| 648 |
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40 | PetscCall(MatProductSetType(G,MATPRODUCT_PtAP)); |
| 649 |
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40 | PetscCall(MatProductSetFromOptions(G)); |
| 650 |
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40 | PetscCall(MatProductSymbolic(G)); |
| 651 |
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40 | PetscCall(MatProductNumeric(G)); |
| 652 |
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40 | PetscCall(MatProductClear(G)); |
| 653 |
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40 | PetscCall(DSRestoreMat(ds,DS_MAT_X,&X)); |
| 654 |
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40 | PetscCall(MatDestroy(&Xt)); |
| 655 |
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40 | PetscCall(MatDestroy(&M)); |
| 656 | /* B = B*G */ | ||
| 657 |
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40 | PetscCall(BVMultInPlace(bv,G,bv->l,bv->k)); |
| 658 |
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40 | PetscCall(MatDestroy(&G)); |
| 659 |
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40 | PetscCall(PetscFree(eig)); |
| 660 |
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40 | PetscCall(DSDestroy(&ds)); |
| 661 |
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40 | PetscCall(PetscLogEventEnd(BV_SetRandom,bv,0,0,0)); |
| 662 |
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40 | PetscCall(PetscObjectStateIncrease((PetscObject)bv)); |
| 663 |
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40 | PetscFunctionReturn(PETSC_SUCCESS); |
| 664 | } | ||
| 665 | |||
| 666 | /*@ | ||
| 667 | BVMatMult - Computes the matrix-vector product for each column, Y=A*V. | ||
| 668 | |||
| 669 | Neighbor-wise Collective | ||
| 670 | |||
| 671 | Input Parameters: | ||
| 672 | + V - basis vectors context | ||
| 673 | - A - the matrix | ||
| 674 | |||
| 675 | Output Parameter: | ||
| 676 | . Y - the result | ||
| 677 | |||
| 678 | Notes: | ||
| 679 | Both V and Y must be distributed in the same manner. Only active columns | ||
| 680 | (excluding the leading ones) are processed. | ||
| 681 | In the result Y, columns are overwritten starting from the leading ones. | ||
| 682 | The number of active columns in V and Y should match, although they need | ||
| 683 | not be the same columns. | ||
| 684 | |||
| 685 | It is possible to choose whether the computation is done column by column | ||
| 686 | or as a Mat-Mat product, see BVSetMatMultMethod(). | ||
| 687 | |||
| 688 | Level: beginner | ||
| 689 | |||
| 690 | .seealso: BVCopy(), BVSetActiveColumns(), BVMatMultColumn(), BVMatMultTranspose(), BVMatMultHermitianTranspose(), BVSetMatMultMethod() | ||
| 691 | @*/ | ||
| 692 | 116545 | PetscErrorCode BVMatMult(BV V,Mat A,BV Y) | |
| 693 | { | ||
| 694 | 116545 | PetscInt M,N,m,n; | |
| 695 | |||
| 696 |
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116545 | PetscFunctionBegin; |
| 697 |
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116545 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 698 |
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116545 | PetscValidType(V,1); |
| 699 |
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116545 | BVCheckSizes(V,1); |
| 700 |
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116545 | BVCheckOp(V,1,matmult); |
| 701 |
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116545 | PetscValidHeaderSpecific(A,MAT_CLASSID,2); |
| 702 |
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116545 | PetscValidType(A,2); |
| 703 |
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116545 | PetscValidHeaderSpecific(Y,BV_CLASSID,3); |
| 704 |
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116545 | PetscValidType(Y,3); |
| 705 |
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116545 | BVCheckSizes(Y,3); |
| 706 |
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116545 | PetscCheckSameComm(V,1,A,2); |
| 707 |
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116545 | PetscCheckSameTypeAndComm(V,1,Y,3); |
| 708 | |||
| 709 |
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116545 | PetscCall(MatGetSize(A,&M,&N)); |
| 710 |
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116545 | PetscCall(MatGetLocalSize(A,&m,&n)); |
| 711 |
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116545 | PetscCheck(M==Y->N,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching row dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,M,Y->N); |
| 712 |
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116545 | PetscCheck(m==Y->n,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching local row dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,m,Y->n); |
| 713 |
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116545 | PetscCheck(N==V->N,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching column dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,N,V->N); |
| 714 |
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116545 | PetscCheck(n==V->n,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching local column dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,n,V->n); |
| 715 |
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116545 | PetscCheck(V->k-V->l==Y->k-Y->l,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Y has %" PetscInt_FMT " active columns, should match %" PetscInt_FMT " active columns in V",Y->k-Y->l,V->k-V->l); |
| 716 | |||
| 717 |
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116545 | PetscCall(PetscLogEventBegin(BV_MatMult,V,A,Y,0)); |
| 718 |
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116545 | PetscUseTypeMethod(V,matmult,A,Y); |
| 719 |
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116545 | PetscCall(PetscLogEventEnd(BV_MatMult,V,A,Y,0)); |
| 720 |
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116545 | PetscCall(PetscObjectStateIncrease((PetscObject)Y)); |
| 721 |
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|
116545 | PetscFunctionReturn(PETSC_SUCCESS); |
| 722 | } | ||
| 723 | |||
| 724 | /*@ | ||
| 725 | BVMatMultTranspose - Computes the matrix-vector product with the transpose | ||
| 726 | of a matrix for each column, Y=A^T*V. | ||
| 727 | |||
| 728 | Neighbor-wise Collective | ||
| 729 | |||
| 730 | Input Parameters: | ||
| 731 | + V - basis vectors context | ||
| 732 | - A - the matrix | ||
| 733 | |||
| 734 | Output Parameter: | ||
| 735 | . Y - the result | ||
| 736 | |||
| 737 | Notes: | ||
| 738 | Both V and Y must be distributed in the same manner. Only active columns | ||
| 739 | (excluding the leading ones) are processed. | ||
| 740 | In the result Y, columns are overwritten starting from the leading ones. | ||
| 741 | The number of active columns in V and Y should match, although they need | ||
| 742 | not be the same columns. | ||
| 743 | |||
| 744 | Currently implemented via MatCreateTranspose(). | ||
| 745 | |||
| 746 | Level: beginner | ||
| 747 | |||
| 748 | .seealso: BVMatMult(), BVMatMultHermitianTranspose() | ||
| 749 | @*/ | ||
| 750 | 80 | PetscErrorCode BVMatMultTranspose(BV V,Mat A,BV Y) | |
| 751 | { | ||
| 752 | 80 | PetscInt M,N,m,n; | |
| 753 | 80 | Mat AT; | |
| 754 | |||
| 755 |
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80 | PetscFunctionBegin; |
| 756 |
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80 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 757 |
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80 | PetscValidType(V,1); |
| 758 |
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80 | BVCheckSizes(V,1); |
| 759 |
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80 | PetscValidHeaderSpecific(A,MAT_CLASSID,2); |
| 760 |
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80 | PetscValidType(A,2); |
| 761 |
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80 | PetscValidHeaderSpecific(Y,BV_CLASSID,3); |
| 762 |
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80 | PetscValidType(Y,3); |
| 763 |
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80 | BVCheckSizes(Y,3); |
| 764 |
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80 | PetscCheckSameComm(V,1,A,2); |
| 765 |
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80 | PetscCheckSameTypeAndComm(V,1,Y,3); |
| 766 | |||
| 767 |
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80 | PetscCall(MatGetSize(A,&M,&N)); |
| 768 |
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80 | PetscCall(MatGetLocalSize(A,&m,&n)); |
| 769 |
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80 | PetscCheck(M==V->N,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching row dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,M,V->N); |
| 770 |
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80 | PetscCheck(m==V->n,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching local row dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,m,V->n); |
| 771 |
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80 | PetscCheck(N==Y->N,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching column dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,N,Y->N); |
| 772 |
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80 | PetscCheck(n==Y->n,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching local column dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,n,Y->n); |
| 773 |
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80 | PetscCheck(V->k-V->l==Y->k-Y->l,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Y has %" PetscInt_FMT " active columns, should match %" PetscInt_FMT " active columns in V",Y->k-Y->l,V->k-V->l); |
| 774 | |||
| 775 |
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80 | PetscCall(MatCreateTranspose(A,&AT)); |
| 776 |
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80 | PetscCall(BVMatMult(V,AT,Y)); |
| 777 |
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80 | PetscCall(MatDestroy(&AT)); |
| 778 |
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16 | PetscFunctionReturn(PETSC_SUCCESS); |
| 779 | } | ||
| 780 | |||
| 781 | /*@ | ||
| 782 | BVMatMultHermitianTranspose - Computes the matrix-vector product with the | ||
| 783 | conjugate transpose of a matrix for each column, Y=A^H*V. | ||
| 784 | |||
| 785 | Neighbor-wise Collective | ||
| 786 | |||
| 787 | Input Parameters: | ||
| 788 | + V - basis vectors context | ||
| 789 | - A - the matrix | ||
| 790 | |||
| 791 | Output Parameter: | ||
| 792 | . Y - the result | ||
| 793 | |||
| 794 | Note: | ||
| 795 | Both V and Y must be distributed in the same manner. Only active columns | ||
| 796 | (excluding the leading ones) are processed. | ||
| 797 | In the result Y, columns are overwritten starting from the leading ones. | ||
| 798 | The number of active columns in V and Y should match, although they need | ||
| 799 | not be the same columns. | ||
| 800 | |||
| 801 | Currently implemented via MatCreateHermitianTranspose(). | ||
| 802 | |||
| 803 | Level: beginner | ||
| 804 | |||
| 805 | .seealso: BVMatMult(), BVMatMultTranspose() | ||
| 806 | @*/ | ||
| 807 | 806 | PetscErrorCode BVMatMultHermitianTranspose(BV V,Mat A,BV Y) | |
| 808 | { | ||
| 809 | 806 | PetscInt j,M,N,m,n; | |
| 810 | 806 | Vec v,y; | |
| 811 | |||
| 812 |
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806 | PetscFunctionBegin; |
| 813 |
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806 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 814 |
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806 | PetscValidType(V,1); |
| 815 |
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806 | BVCheckSizes(V,1); |
| 816 |
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806 | PetscValidHeaderSpecific(A,MAT_CLASSID,2); |
| 817 |
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806 | PetscValidType(A,2); |
| 818 |
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806 | PetscValidHeaderSpecific(Y,BV_CLASSID,3); |
| 819 |
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806 | PetscValidType(Y,3); |
| 820 |
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806 | BVCheckSizes(Y,3); |
| 821 |
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806 | PetscCheckSameComm(V,1,A,2); |
| 822 |
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806 | PetscCheckSameTypeAndComm(V,1,Y,3); |
| 823 | |||
| 824 |
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806 | PetscCall(MatGetSize(A,&M,&N)); |
| 825 |
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806 | PetscCall(MatGetLocalSize(A,&m,&n)); |
| 826 |
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806 | PetscCheck(M==V->N,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching row dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,M,V->N); |
| 827 |
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806 | PetscCheck(m==V->n,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_INCOMP,"Mismatching local row dimension A %" PetscInt_FMT ", V %" PetscInt_FMT,m,V->n); |
| 828 |
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806 | PetscCheck(N==Y->N,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching column dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,N,Y->N); |
| 829 |
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806 | PetscCheck(n==Y->n,PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_INCOMP,"Mismatching local column dimension A %" PetscInt_FMT ", Y %" PetscInt_FMT,n,Y->n); |
| 830 |
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806 | PetscCheck(V->k-V->l==Y->k-Y->l,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_SIZ,"Y has %" PetscInt_FMT " active columns, should match %" PetscInt_FMT " active columns in V",Y->k-Y->l,V->k-V->l); |
| 831 | |||
| 832 | /* TODO: recover this code if PETSc ever gets MATPRODUCT_AhB done | ||
| 833 | PetscCall(MatCreateHermitianTranspose(A,&AH)); | ||
| 834 | PetscCall(BVMatMult(V,AH,Y)); | ||
| 835 | PetscCall(MatDestroy(&AH)); | ||
| 836 | */ | ||
| 837 |
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2944 | for (j=0;j<V->k-V->l;j++) { |
| 838 |
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2138 | PetscCall(BVGetColumn(V,V->l+j,&v)); |
| 839 |
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2138 | PetscCall(BVGetColumn(Y,Y->l+j,&y)); |
| 840 |
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|
2138 | PetscCall(MatMultHermitianTranspose(A,v,y)); |
| 841 |
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|
2138 | PetscCall(BVRestoreColumn(V,V->l+j,&v)); |
| 842 |
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2138 | PetscCall(BVRestoreColumn(Y,Y->l+j,&y)); |
| 843 | } | ||
| 844 |
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|
158 | PetscFunctionReturn(PETSC_SUCCESS); |
| 845 | } | ||
| 846 | |||
| 847 | /*@ | ||
| 848 | BVMatMultColumn - Computes the matrix-vector product for a specified | ||
| 849 | column, storing the result in the next column v_{j+1}=A*v_j. | ||
| 850 | |||
| 851 | Neighbor-wise Collective | ||
| 852 | |||
| 853 | Input Parameters: | ||
| 854 | + V - basis vectors context | ||
| 855 | . A - the matrix | ||
| 856 | - j - the column | ||
| 857 | |||
| 858 | Level: beginner | ||
| 859 | |||
| 860 | .seealso: BVMatMult(), BVMatMultTransposeColumn(), BVMatMultHermitianTransposeColumn() | ||
| 861 | @*/ | ||
| 862 | 727395 | PetscErrorCode BVMatMultColumn(BV V,Mat A,PetscInt j) | |
| 863 | { | ||
| 864 | 727395 | Vec vj,vj1; | |
| 865 | |||
| 866 |
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727395 | PetscFunctionBegin; |
| 867 |
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727395 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 868 |
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727395 | PetscValidType(V,1); |
| 869 |
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727395 | BVCheckSizes(V,1); |
| 870 |
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727395 | PetscValidHeaderSpecific(A,MAT_CLASSID,2); |
| 871 |
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|
727395 | PetscCheckSameComm(V,1,A,2); |
| 872 |
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|
727395 | PetscValidLogicalCollectiveInt(V,j,3); |
| 873 |
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727395 | PetscCheck(j>=0,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Index j must be non-negative"); |
| 874 |
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727395 | PetscCheck(j+1<V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Result should go in index j+1=%" PetscInt_FMT " but BV only has %" PetscInt_FMT " columns",j+1,V->m); |
| 875 | |||
| 876 |
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727395 | PetscCall(PetscLogEventBegin(BV_MatMultVec,V,A,0,0)); |
| 877 |
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727395 | PetscCall(BVGetColumn(V,j,&vj)); |
| 878 |
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727395 | PetscCall(BVGetColumn(V,j+1,&vj1)); |
| 879 |
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727395 | PetscCall(MatMult(A,vj,vj1)); |
| 880 |
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727395 | PetscCall(BVRestoreColumn(V,j,&vj)); |
| 881 |
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727395 | PetscCall(BVRestoreColumn(V,j+1,&vj1)); |
| 882 |
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727395 | PetscCall(PetscLogEventEnd(BV_MatMultVec,V,A,0,0)); |
| 883 |
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727395 | PetscCall(PetscObjectStateIncrease((PetscObject)V)); |
| 884 |
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|
727395 | PetscFunctionReturn(PETSC_SUCCESS); |
| 885 | } | ||
| 886 | |||
| 887 | /*@ | ||
| 888 | BVMatMultTransposeColumn - Computes the transpose matrix-vector product for a | ||
| 889 | specified column, storing the result in the next column v_{j+1}=A^T*v_j. | ||
| 890 | |||
| 891 | Neighbor-wise Collective | ||
| 892 | |||
| 893 | Input Parameters: | ||
| 894 | + V - basis vectors context | ||
| 895 | . A - the matrix | ||
| 896 | - j - the column | ||
| 897 | |||
| 898 | Level: beginner | ||
| 899 | |||
| 900 | .seealso: BVMatMult(), BVMatMultColumn(), BVMatMultHermitianTransposeColumn() | ||
| 901 | @*/ | ||
| 902 | 80 | PetscErrorCode BVMatMultTransposeColumn(BV V,Mat A,PetscInt j) | |
| 903 | { | ||
| 904 | 80 | Vec vj,vj1; | |
| 905 | |||
| 906 |
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80 | PetscFunctionBegin; |
| 907 |
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80 | PetscValidHeaderSpecific(V,BV_CLASSID,1); |
| 908 |
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80 | PetscValidType(V,1); |
| 909 |
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80 | BVCheckSizes(V,1); |
| 910 |
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80 | PetscValidHeaderSpecific(A,MAT_CLASSID,2); |
| 911 |
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80 | PetscCheckSameComm(V,1,A,2); |
| 912 |
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80 | PetscValidLogicalCollectiveInt(V,j,3); |
| 913 |
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80 | PetscCheck(j>=0,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Index j must be non-negative"); |
| 914 |
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80 | PetscCheck(j+1<V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Result should go in index j+1=%" PetscInt_FMT " but BV only has %" PetscInt_FMT " columns",j+1,V->m); |
| 915 | |||
| 916 |
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80 | PetscCall(PetscLogEventBegin(BV_MatMultVec,V,A,0,0)); |
| 917 |
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80 | PetscCall(BVGetColumn(V,j,&vj)); |
| 918 |
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80 | PetscCall(BVGetColumn(V,j+1,&vj1)); |
| 919 |
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80 | PetscCall(MatMultTranspose(A,vj,vj1)); |
| 920 |
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80 | PetscCall(BVRestoreColumn(V,j,&vj)); |
| 921 |
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80 | PetscCall(BVRestoreColumn(V,j+1,&vj1)); |
| 922 |
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80 | PetscCall(PetscLogEventEnd(BV_MatMultVec,V,A,0,0)); |
| 923 |
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80 | PetscCall(PetscObjectStateIncrease((PetscObject)V)); |
| 924 |
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80 | PetscFunctionReturn(PETSC_SUCCESS); |
| 925 | } | ||
| 926 | |||
| 927 | /*@ | ||
| 928 | BVMatMultHermitianTransposeColumn - Computes the conjugate-transpose matrix-vector | ||
| 929 | product for a specified column, storing the result in the next column v_{j+1}=A^H*v_j. | ||
| 930 | |||
| 931 | Neighbor-wise Collective | ||
| 932 | |||
| 933 | Input Parameters: | ||
| 934 | + V - basis vectors context | ||
| 935 | . A - the matrix | ||
| 936 | - j - the column | ||
| 937 | |||
| 938 | Level: beginner | ||
| 939 | |||
| 940 | .seealso: BVMatMult(), BVMatMultColumn(), BVMatMultTransposeColumn() | ||
| 941 | @*/ | ||
| 942 | ✗ | PetscErrorCode BVMatMultHermitianTransposeColumn(BV V,Mat A,PetscInt j) | |
| 943 | { | ||
| 944 | ✗ | Vec vj,vj1; | |
| 945 | |||
| 946 | ✗ | PetscFunctionBegin; | |
| 947 | ✗ | PetscValidHeaderSpecific(V,BV_CLASSID,1); | |
| 948 | ✗ | PetscValidType(V,1); | |
| 949 | ✗ | BVCheckSizes(V,1); | |
| 950 | ✗ | PetscValidHeaderSpecific(A,MAT_CLASSID,2); | |
| 951 | ✗ | PetscCheckSameComm(V,1,A,2); | |
| 952 | ✗ | PetscValidLogicalCollectiveInt(V,j,3); | |
| 953 | ✗ | PetscCheck(j>=0,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Index j must be non-negative"); | |
| 954 | ✗ | PetscCheck(j+1<V->m,PetscObjectComm((PetscObject)V),PETSC_ERR_ARG_OUTOFRANGE,"Result should go in index j+1=%" PetscInt_FMT " but BV only has %" PetscInt_FMT " columns",j+1,V->m); | |
| 955 | |||
| 956 | ✗ | PetscCall(PetscLogEventBegin(BV_MatMultVec,V,A,0,0)); | |
| 957 | ✗ | PetscCall(BVGetColumn(V,j,&vj)); | |
| 958 | ✗ | PetscCall(BVGetColumn(V,j+1,&vj1)); | |
| 959 | ✗ | PetscCall(MatMultHermitianTranspose(A,vj,vj1)); | |
| 960 | ✗ | PetscCall(BVRestoreColumn(V,j,&vj)); | |
| 961 | ✗ | PetscCall(BVRestoreColumn(V,j+1,&vj1)); | |
| 962 | ✗ | PetscCall(PetscLogEventEnd(BV_MatMultVec,V,A,0,0)); | |
| 963 | ✗ | PetscCall(PetscObjectStateIncrease((PetscObject)V)); | |
| 964 | ✗ | PetscFunctionReturn(PETSC_SUCCESS); | |
| 965 | } | ||
| 966 |