GCC Code Coverage Report

Directory: ./
File: src/svd/impls/lanczos/gklanczos.c
Date: 2025-12-10 04:20:18
Exec Total Coverage
Lines: 249 265 94.0%
Functions: 12 13 92.3%
Branches: 685 1194 57.4%
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
37 215 static PetscErrorCode SVDSetUp_Lanczos(SVD svd)
38 {
39 215 SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data;
40 215 PetscInt N;
41
42
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 215 PetscFunctionBegin;
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 215 SVDCheckStandard(svd);
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 215 SVDCheckDefinite(svd);
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 215 PetscCall(MatGetSize(svd->A,NULL,&N));
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 215 PetscCall(SVDSetDimensions_Default(svd));
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 215 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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 314 if (svd->max_it==PETSC_DETERMINE) svd->max_it = PetscMax(N/svd->ncv,100)*((svd->stop==SVD_STOP_THRESHOLD)?10:1);
49 215 svd->leftbasis = PetscNot(lanczos->oneside);
50
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 215 PetscCall(SVDAllocateSolution(svd,1));
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 215 PetscCall(DSSetType(svd->ds,DSSVD));
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 215 PetscCall(DSSetCompact(svd->ds,PETSC_TRUE));
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 215 PetscCall(DSSetExtraRow(svd->ds,PETSC_TRUE));
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 215 PetscCall(DSAllocate(svd->ds,svd->ncv+1));
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 41 PetscFunctionReturn(PETSC_SUCCESS);
56 }
57
58 13211 PetscErrorCode SVDTwoSideLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,BV U,PetscInt k,PetscInt *n,PetscBool *breakdown)
59 {
60 13211 PetscInt i;
61 13211 Vec u,v;
62 13211 PetscBool lindep=PETSC_FALSE;
63
64
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 13211 PetscFunctionBegin;
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 13211 PetscCall(BVGetColumn(svd->V,k,&v));
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 13211 PetscCall(BVGetColumn(svd->U,k,&u));
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 13211 PetscCall(MatMult(svd->A,v,u));
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 13211 PetscCall(BVRestoreColumn(svd->V,k,&v));
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 13211 PetscCall(BVRestoreColumn(svd->U,k,&u));
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 13211 PetscCall(BVOrthonormalizeColumn(svd->U,k,PETSC_FALSE,alpha+k,&lindep));
71
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 13211 if (PetscUnlikely(lindep)) {
72 ✗ *n = k;
73 ✗ if (breakdown) *breakdown = lindep;
74 ✗ PetscFunctionReturn(PETSC_SUCCESS);
75 }
76
77
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 94381 for (i=k+1;i<*n;i++) {
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 81170 PetscCall(BVGetColumn(svd->V,i,&v));
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 81170 PetscCall(BVGetColumn(svd->U,i-1,&u));
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 81170 PetscCall(MatMult(svd->AT,u,v));
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 81170 PetscCall(BVRestoreColumn(svd->V,i,&v));
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 81170 PetscCall(BVRestoreColumn(svd->U,i-1,&u));
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 81170 PetscCall(BVOrthonormalizeColumn(svd->V,i,PETSC_FALSE,beta+i-1,&lindep));
84
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 81170 if (PetscUnlikely(lindep)) {
85 ✗ *n = i;
86 ✗ break;
87 }
88
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 81170 PetscCall(BVGetColumn(svd->V,i,&v));
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 81170 PetscCall(BVGetColumn(svd->U,i,&u));
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 81170 PetscCall(MatMult(svd->A,v,u));
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 81170 PetscCall(BVRestoreColumn(svd->V,i,&v));
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 81170 PetscCall(BVRestoreColumn(svd->U,i,&u));
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 81170 PetscCall(BVOrthonormalizeColumn(svd->U,i,PETSC_FALSE,alpha+i,&lindep));
94
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 81170 if (PetscUnlikely(lindep)) {
95 ✗ *n = i;
96 ✗ break;
97 }
98 }
99
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 13211 if (!lindep) {
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 13211 PetscCall(BVGetColumn(svd->V,*n,&v));
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 13211 PetscCall(BVGetColumn(svd->U,*n-1,&u));
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 13211 PetscCall(MatMult(svd->AT,u,v));
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 13211 PetscCall(BVRestoreColumn(svd->V,*n,&v));
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 13211 PetscCall(BVRestoreColumn(svd->U,*n-1,&u));
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 13211 PetscCall(BVOrthogonalizeColumn(svd->V,*n,NULL,beta+*n-1,&lindep));
107 }
108
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 13211 if (breakdown) *breakdown = lindep;
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 1943 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;
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 688 PetscCall(BVGetActiveColumns(V,&bvl,&bvk));
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 688 PetscCall(BVGetColumn(V,k,&z));
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 688 PetscCall(MatMult(svd->A,z,u));
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 688 PetscCall(BVRestoreColumn(V,k,&z));
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 5980 for (i=k+1;i<n;i++) {
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 5292 PetscCall(BVGetColumn(V,i,&z));
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 5292 PetscCall(MatMult(svd->AT,u,z));
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 5292 PetscCall(BVRestoreColumn(V,i,&z));
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 5292 PetscCall(VecNormBegin(u,NORM_2,&a));
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 5292 PetscCall(BVSetActiveColumns(V,0,i));
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 5292 PetscCall(BVDotColumnBegin(V,i,work));
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 5292 PetscCall(VecNormEnd(u,NORM_2,&a));
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 5292 PetscCall(BVDotColumnEnd(V,i,work));
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 5292 PetscCall(VecScale(u,1.0/a));
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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 */
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 5292 PetscCall(BVDotColumn(V,i,work));
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 5292 PetscCall(BVMultColumn(V,-1.0,1.0,i,work));
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 5292 PetscCall(BVNormColumn(V,i,NORM_2,&b));
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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));
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 5292 PetscCall(BVGetColumn(V,i,&z));
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 5292 PetscCall(MatMult(svd->A,z,u_1));
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 5292 PetscCall(BVRestoreColumn(V,i,&z));
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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
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 688 PetscCall(BVGetColumn(V,n,&z));
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 688 PetscCall(MatMult(svd->AT,u,z));
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 688 PetscCall(BVRestoreColumn(V,n,&z));
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 688 PetscCall(VecNormBegin(u,NORM_2,&a));
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 688 PetscCall(BVDotColumnBegin(V,n,work));
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 688 PetscCall(VecNormEnd(u,NORM_2,&a));
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 688 PetscCall(BVDotColumnEnd(V,n,work));
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 688 PetscCall(VecScale(u,1.0/a));
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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 */
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 688 PetscCall(BVDotColumn(V,n,work));
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 688 PetscCall(BVMultColumn(V,-1.0,1.0,n,work));
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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 25380 PetscErrorCode SVDKrylovConvergence(SVD svd,PetscBool getall,PetscInt kini,PetscInt nits,PetscReal normr,PetscInt *kout)
190 {
191 25380 PetscInt k,marker,ld;
192 25380 PetscReal *alpha,*beta,*betah,resnorm;
193 25380 PetscBool extra;
194
195
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 25380 PetscFunctionBegin;
196
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 25380 if (PetscUnlikely(svd->conv == SVD_CONV_MAXIT && svd->its >= svd->max_it)) *kout = svd->nsv;
197 else {
198
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 25380 PetscCall(DSGetLeadingDimension(svd->ds,&ld));
199
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 25380 PetscCall(DSGetExtraRow(svd->ds,&extra));
200
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 25380 PetscCheck(extra,PetscObjectComm((PetscObject)svd),PETSC_ERR_SUP,"Only implemented for DS with extra row");
201 25380 marker = -1;
202
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 25380 if (svd->trackall) getall = PETSC_TRUE;
203
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 25380 PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha));
204 25380 beta = alpha + ld;
205 25380 betah = alpha + 2*ld;
206
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 32198 for (k=kini;k<kini+nits;k++) {
207
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 32147 if (svd->isgeneralized) resnorm = SlepcAbs(beta[k],betah[k])*normr;
208 21038 else resnorm = PetscAbsReal(beta[k]);
209
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 32147 PetscCall((*svd->converged)(svd,svd->sigma[k],resnorm,&svd->errest[k],svd->convergedctx));
210
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 32147 if (marker==-1 && svd->errest[k] >= svd->tol) marker = k;
211
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 32147 if (marker!=-1 && !getall) break;
212 }
213
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 25380 PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha));
214
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 25380 if (marker!=-1) k = marker;
215 25380 *kout = k;
216 }
217
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 4295 PetscFunctionReturn(PETSC_SUCCESS);
218 }
219
220 215 static PetscErrorCode SVDSolve_Lanczos(SVD svd)
221 {
222 215 SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data;
223 215 PetscReal *alpha,*beta;
224 215 PetscScalar *swork,*w,*P,*aux1,*aux2;
225 215 PetscInt i,k,j,nv,ld;
226 215 Vec u=NULL,u_1=NULL;
227 215 Mat U,V;
228
229
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 215 PetscFunctionBegin;
230 /* allocate working space */
231
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 215 PetscCall(DSGetLeadingDimension(svd->ds,&ld));
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 215 PetscCall(PetscMalloc2(ld,&w,svd->ncv,&swork));
233
234
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 215 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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 215 if (!svd->nini) {
241
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 167 PetscCall(BVSetRandomColumn(svd->V,0));
242
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 167 PetscCall(BVOrthonormalizeColumn(svd->V,0,PETSC_TRUE,NULL,NULL));
243 }
244
245 6874 while (svd->reason == SVD_CONVERGED_ITERATING) {
246 6659 svd->its++;
247
248 /* inner loop */
249 6659 nv = PetscMin(svd->nconv+svd->mpd,svd->ncv);
250
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 6659 PetscCall(DSGetArrayReal(svd->ds,DS_MAT_T,&alpha));
251 6659 beta = alpha + ld;
252
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 6659 if (lanczos->oneside) PetscCall(SVDOneSideLanczos(svd,alpha,beta,svd->V,u,u_1,svd->nconv,nv,swork));
253 else {
254
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 5971 PetscCall(SVDTwoSideLanczos(svd,alpha,beta,svd->V,svd->U,svd->nconv,&nv,NULL));
255
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 5971 PetscCall(BVSetActiveColumns(svd->U,svd->nconv,nv));
256 }
257
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 6659 PetscCall(DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha));
258
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 6659 PetscCall(BVSetActiveColumns(svd->V,svd->nconv,nv));
259
260 /* compute SVD of bidiagonal matrix */
261
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 6659 PetscCall(DSSetDimensions(svd->ds,nv,svd->nconv,0));
262
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 6659 PetscCall(DSSVDSetDimensions(svd->ds,nv));
263
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 6659 PetscCall(DSSetState(svd->ds,DS_STATE_INTERMEDIATE));
264
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 6659 PetscCall(DSSolve(svd->ds,w,NULL));
265
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 6659 PetscCall(DSSort(svd->ds,w,NULL,NULL,NULL,NULL));
266
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 6659 PetscCall(DSUpdateExtraRow(svd->ds));
267
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 6659 PetscCall(DSSynchronize(svd->ds,w,NULL));
268
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 69157 for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]);
269
270 /* check convergence */
271
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 6659 PetscCall(SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,1.0,&k));
272
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 6659 SVDSetCtxThreshold(svd,svd->sigma,k);
273
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 6659 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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 6659 if (svd->reason == SVD_CONVERGED_ITERATING) {
277
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 6444 if (k<nv) {
278
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 6444 PetscCall(DSGetArray(svd->ds,DS_MAT_V,&P));
279
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 67013 for (j=svd->nconv;j<nv;j++) swork[j-svd->nconv] = PetscConj(P[j+k*ld]);
280
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 6444 PetscCall(DSRestoreArray(svd->ds,DS_MAT_V,&P));
281
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 6444 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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 6659 PetscCall(DSGetMat(svd->ds,DS_MAT_V,&V));
291
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 6659 PetscCall(BVMultInPlace(svd->V,V,svd->nconv,k));
292
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 6659 PetscCall(DSRestoreMat(svd->ds,DS_MAT_V,&V));
293
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 6659 if (!lanczos->oneside) {
294
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 5971 PetscCall(DSGetMat(svd->ds,DS_MAT_U,&U));
295
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 5971 PetscCall(BVMultInPlace(svd->U,U,svd->nconv,k));
296
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 5971 PetscCall(DSRestoreMat(svd->ds,DS_MAT_U,&U));
297 }
298
299
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 6659 if (svd->reason == SVD_CONVERGED_ITERATING) {
300
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 6444 PetscCall(BVCopyColumn(svd->V,nv,k)); /* copy restart vector from the last column */
301
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 6444 if (svd->stop==SVD_STOP_THRESHOLD && nv-k<5) { /* reallocate */
302 8 svd->ncv = svd->mpd+k;
303
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 8 PetscCall(SVDReallocateSolution(svd,svd->ncv+1));
304
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 66 for (i=nv;i<svd->ncv;i++) svd->perm[i] = i;
305
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 8 PetscCall(DSReallocate(svd->ds,svd->ncv+1));
306 8 aux1 = w;
307 8 aux2 = swork;
308
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 8 PetscCall(PetscMalloc2(svd->ncv+1,&w,svd->ncv,&swork));
309
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 8 PetscCall(PetscArraycpy(w,aux1,ld));
310
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 8 PetscCall(PetscFree2(aux1,aux2));
311
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 8 PetscCall(DSGetLeadingDimension(svd->ds,&ld));
312 }
313 }
314
315 6659 svd->nconv = k;
316
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 6874 PetscCall(SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,nv));
317 }
318
319 /* free working space */
320
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 215 PetscCall(VecDestroy(&u));
321
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 215 PetscCall(VecDestroy(&u_1));
322
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 215 PetscCall(PetscFree2(w,swork));
323
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 41 PetscFunctionReturn(PETSC_SUCCESS);
324 }
325
326 155 static PetscErrorCode SVDSetFromOptions_Lanczos(SVD svd,PetscOptionItems PetscOptionsObject)
327 {
328 155 PetscBool set,val;
329 155 SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data;
330
331
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 155 PetscFunctionBegin;
332
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 155 PetscOptionsHeadBegin(PetscOptionsObject,"SVD Lanczos Options");
333
334
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 155 PetscCall(PetscOptionsBool("-svd_lanczos_oneside","Use one-side reorthogonalization","SVDLanczosSetOneSide",lanczos->oneside,&val,&set));
335
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 155 if (set) PetscCall(SVDLanczosSetOneSide(svd,val));
336
337
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 155 PetscOptionsHeadEnd();
338
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 29 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 - 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);
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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
420 175 static PetscErrorCode SVDDestroy_Lanczos(SVD svd)
421 {
422
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 175 PetscFunctionBegin;
423
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 175 PetscCall(PetscFree(svd->data));
424
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 175 PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",NULL));
425
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 175 PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",NULL));
426
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 33 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*/
461 175 SLEPC_EXTERN PetscErrorCode SVDCreate_Lanczos(SVD svd)
462 {
463 175 SVD_LANCZOS *ctx;
464
465
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 175 PetscFunctionBegin;
466
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 175 PetscCall(PetscNew(&ctx));
467 175 svd->data = (void*)ctx;
468
469 175 svd->ops->setup = SVDSetUp_Lanczos;
470 175 svd->ops->solve = SVDSolve_Lanczos;
471 175 svd->ops->destroy = SVDDestroy_Lanczos;
472 175 svd->ops->setfromoptions = SVDSetFromOptions_Lanczos;
473 175 svd->ops->view = SVDView_Lanczos;
474 175 svd->ops->computevectors = SVDComputeVectors_Left;
475
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 175 PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",SVDLanczosSetOneSide_Lanczos));
476
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 175 PetscCall(PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",SVDLanczosGetOneSide_Lanczos));
477
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 33 PetscFunctionReturn(PETSC_SUCCESS);
478 }
479