Date: 2023-09-05T10:18:40+00:00

Actual source code: svdsolve.c
slepc-3.19.2 2023-09-05
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.
      7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
      8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      9: */
     10: /*
     11:    SVD routines related to the solution process
     12: */

     14: #include <slepc/private/svdimpl.h>
     15: #include <slepc/private/bvimpl.h>

     17: /*
     18:   SVDComputeVectors_Left - Compute left singular vectors as U=A*V.
     19:   Only done if the leftbasis flag is false. Assumes V is available.
     20:  */
     21: PetscErrorCode SVDComputeVectors_Left(SVD svd)
     22: {
     23:   Vec                tl,omega2,u,v,w;
     24:   PetscInt           i,oldsize;
     25:   VecType            vtype;
     26:   const PetscScalar* varray;

     28:   PetscFunctionBegin;
     29:   if (!svd->leftbasis) {
     30:     /* generate left singular vectors on U */
     31:     if (!svd->U) PetscCall(SVDGetBV(svd,NULL,&svd->U));
     32:     PetscCall(BVGetSizes(svd->U,NULL,NULL,&oldsize));
     33:     if (!oldsize) {
     34:       if (!((PetscObject)(svd->U))->type_name) PetscCall(BVSetType(svd->U,((PetscObject)(svd->V))->type_name));
     35:       PetscCall(MatCreateVecsEmpty(svd->A,NULL,&tl));
     36:       PetscCall(BVSetSizesFromVec(svd->U,tl,svd->ncv));
     37:       PetscCall(VecDestroy(&tl));
     38:     }
     39:     PetscCall(BVSetActiveColumns(svd->V,0,svd->nconv));
     40:     PetscCall(BVSetActiveColumns(svd->U,0,svd->nconv));
     41:     if (!svd->ishyperbolic) PetscCall(BVMatMult(svd->V,svd->A,svd->U));
     42:     else if (svd->swapped) {  /* compute right singular vectors as V=A'*Omega*U */
     43:       PetscCall(MatCreateVecs(svd->A,&w,NULL));
     44:       for (i=0;i<svd->nconv;i++) {
     45:         PetscCall(BVGetColumn(svd->V,i,&v));
     46:         PetscCall(BVGetColumn(svd->U,i,&u));
     47:         PetscCall(VecPointwiseMult(w,v,svd->omega));
     48:         PetscCall(MatMult(svd->A,w,u));
     49:         PetscCall(BVRestoreColumn(svd->V,i,&v));
     50:         PetscCall(BVRestoreColumn(svd->U,i,&u));
     51:       }
     52:       PetscCall(VecDestroy(&w));
     53:     } else {  /* compute left singular vectors as usual U=A*V, and set-up Omega-orthogonalization of U */
     54:       PetscCall(BV_SetMatrixDiagonal(svd->U,svd->omega,svd->A));
     55:       PetscCall(BVMatMult(svd->V,svd->A,svd->U));
     56:     }
     57:     PetscCall(BVOrthogonalize(svd->U,NULL));
     58:     if (svd->ishyperbolic && !svd->swapped) {  /* store signature after Omega-orthogonalization */
     59:       PetscCall(MatGetVecType(svd->A,&vtype));
     60:       PetscCall(VecCreate(PETSC_COMM_SELF,&omega2));
     61:       PetscCall(VecSetSizes(omega2,svd->nconv,svd->nconv));
     62:       PetscCall(VecSetType(omega2,vtype));
     63:       PetscCall(BVGetSignature(svd->U,omega2));
     64:       PetscCall(VecGetArrayRead(omega2,&varray));
     65:       for (i=0;i<svd->nconv;i++) {
     66:         svd->sign[i] = PetscRealPart(varray[i]);
     67:         if (PetscRealPart(varray[i])<0.0) PetscCall(BVScaleColumn(svd->U,i,-1.0));
     68:       }
     69:       PetscCall(VecRestoreArrayRead(omega2,&varray));
     70:       PetscCall(VecDestroy(&omega2));
     71:     }
     72:   }
     73:   PetscFunctionReturn(PETSC_SUCCESS);
     74: }

     76: PetscErrorCode SVDComputeVectors(SVD svd)
     77: {
     78:   PetscFunctionBegin;
     79:   SVDCheckSolved(svd,1);
     80:   if (svd->state==SVD_STATE_SOLVED) PetscTryTypeMethod(svd,computevectors);
     81:   svd->state = SVD_STATE_VECTORS;
     82:   PetscFunctionReturn(PETSC_SUCCESS);
     83: }

     85: /*@
     86:    SVDSolve - Solves the singular value problem.

     88:    Collective

     90:    Input Parameter:
     91: .  svd - singular value solver context obtained from SVDCreate()

     93:    Options Database Keys:
     94: +  -svd_view - print information about the solver used
     95: .  -svd_view_mat0 - view the first matrix (A)
     96: .  -svd_view_mat1 - view the second matrix (B)
     97: .  -svd_view_signature - view the signature matrix (omega)
     98: .  -svd_view_vectors - view the computed singular vectors
     99: .  -svd_view_values - view the computed singular values
    100: .  -svd_converged_reason - print reason for convergence, and number of iterations
    101: .  -svd_error_absolute - print absolute errors of each singular triplet
    102: .  -svd_error_relative - print relative errors of each singular triplet
    103: -  -svd_error_norm     - print errors relative to the matrix norms of each singular triplet

    105:    Notes:
    106:    All the command-line options listed above admit an optional argument specifying
    107:    the viewer type and options. For instance, use '-svd_view_mat0 binary:amatrix.bin'
    108:    to save the A matrix to a binary file, '-svd_view_values draw' to draw the computed
    109:    singular values graphically, or '-svd_error_relative :myerr.m:ascii_matlab' to save
    110:    the errors in a file that can be executed in Matlab.

    112:    Level: beginner

    114: .seealso: SVDCreate(), SVDSetUp(), SVDDestroy()
    115: @*/
    116: PetscErrorCode SVDSolve(SVD svd)
    117: {
    118:   PetscInt       i,*workperm;

    120:   PetscFunctionBegin;
    122:   if (svd->state>=SVD_STATE_SOLVED) PetscFunctionReturn(PETSC_SUCCESS);
    123:   PetscCall(PetscLogEventBegin(SVD_Solve,svd,0,0,0));

    125:   /* call setup */
    126:   PetscCall(SVDSetUp(svd));
    127:   svd->its = 0;
    128:   svd->nconv = 0;
    129:   for (i=0;i<svd->ncv;i++) {
    130:     svd->sigma[i]  = 0.0;
    131:     svd->errest[i] = 0.0;
    132:     svd->perm[i]   = i;
    133:   }
    134:   PetscCall(SVDViewFromOptions(svd,NULL,"-svd_view_pre"));

    136:   switch (svd->problem_type) {
    137:     case SVD_STANDARD:
    138:       PetscUseTypeMethod(svd,solve);
    139:       break;
    140:     case SVD_GENERALIZED:
    141:       PetscUseTypeMethod(svd,solveg);
    142:       break;
    143:     case SVD_HYPERBOLIC:
    144:       PetscUseTypeMethod(svd,solveh);
    145:       break;
    146:   }
    147:   svd->state = SVD_STATE_SOLVED;

    149:   /* sort singular triplets */
    150:   if (svd->which == SVD_SMALLEST) PetscCall(PetscSortRealWithPermutation(svd->nconv,svd->sigma,svd->perm));
    151:   else {
    152:     PetscCall(PetscMalloc1(svd->nconv,&workperm));
    153:     for (i=0;i<svd->nconv;i++) workperm[i] = i;
    154:     PetscCall(PetscSortRealWithPermutation(svd->nconv,svd->sigma,workperm));
    155:     for (i=0;i<svd->nconv;i++) svd->perm[i] = workperm[svd->nconv-i-1];
    156:     PetscCall(PetscFree(workperm));
    157:   }
    158:   PetscCall(PetscLogEventEnd(SVD_Solve,svd,0,0,0));

    160:   /* various viewers */
    161:   PetscCall(SVDViewFromOptions(svd,NULL,"-svd_view"));
    162:   PetscCall(SVDConvergedReasonViewFromOptions(svd));
    163:   PetscCall(SVDErrorViewFromOptions(svd));
    164:   PetscCall(SVDValuesViewFromOptions(svd));
    165:   PetscCall(SVDVectorsViewFromOptions(svd));
    166:   PetscCall(MatViewFromOptions(svd->OP,(PetscObject)svd,"-svd_view_mat0"));
    167:   if (svd->isgeneralized) PetscCall(MatViewFromOptions(svd->OPb,(PetscObject)svd,"-svd_view_mat1"));
    168:   if (svd->ishyperbolic) PetscCall(VecViewFromOptions(svd->omega,(PetscObject)svd,"-svd_view_signature"));

    170:   /* Remove the initial subspaces */
    171:   svd->nini = 0;
    172:   svd->ninil = 0;
    173:   PetscFunctionReturn(PETSC_SUCCESS);
    174: }

    176: /*@
    177:    SVDGetIterationNumber - Gets the current iteration number. If the
    178:    call to SVDSolve() is complete, then it returns the number of iterations
    179:    carried out by the solution method.

    181:    Not Collective

    183:    Input Parameter:
    184: .  svd - the singular value solver context

    186:    Output Parameter:
    187: .  its - number of iterations

    189:    Note:
    190:    During the i-th iteration this call returns i-1. If SVDSolve() is
    191:    complete, then parameter "its" contains either the iteration number at
    192:    which convergence was successfully reached, or failure was detected.
    193:    Call SVDGetConvergedReason() to determine if the solver converged or
    194:    failed and why.

    196:    Level: intermediate

    198: .seealso: SVDGetConvergedReason(), SVDSetTolerances()
    199: @*/
    200: PetscErrorCode SVDGetIterationNumber(SVD svd,PetscInt *its)
    201: {
    202:   PetscFunctionBegin;
    205:   *its = svd->its;
    206:   PetscFunctionReturn(PETSC_SUCCESS);
    207: }

    209: /*@
    210:    SVDGetConvergedReason - Gets the reason why the SVDSolve() iteration was
    211:    stopped.

    213:    Not Collective

    215:    Input Parameter:
    216: .  svd - the singular value solver context

    218:    Output Parameter:
    219: .  reason - negative value indicates diverged, positive value converged
    220:    (see SVDConvergedReason)

    222:    Options Database Key:
    223: .  -svd_converged_reason - print the reason to a viewer

    225:    Notes:
    226:    Possible values for reason are
    227: +  SVD_CONVERGED_TOL - converged up to tolerance
    228: .  SVD_CONVERGED_USER - converged due to a user-defined condition
    229: .  SVD_CONVERGED_MAXIT - reached the maximum number of iterations with SVD_CONV_MAXIT criterion
    230: .  SVD_DIVERGED_ITS - required more than max_it iterations to reach convergence
    231: -  SVD_DIVERGED_BREAKDOWN - generic breakdown in method

    233:    Can only be called after the call to SVDSolve() is complete.

    235:    Level: intermediate

    237: .seealso: SVDSetTolerances(), SVDSolve(), SVDConvergedReason
    238: @*/
    239: PetscErrorCode SVDGetConvergedReason(SVD svd,SVDConvergedReason *reason)
    240: {
    241:   PetscFunctionBegin;
    244:   SVDCheckSolved(svd,1);
    245:   *reason = svd->reason;
    246:   PetscFunctionReturn(PETSC_SUCCESS);
    247: }

    249: /*@
    250:    SVDGetConverged - Gets the number of converged singular values.

    252:    Not Collective

    254:    Input Parameter:
    255: .  svd - the singular value solver context

    257:    Output Parameter:
    258: .  nconv - number of converged singular values

    260:    Note:
    261:    This function should be called after SVDSolve() has finished.

    263:    Level: beginner

    265: .seealso: SVDSetDimensions(), SVDSolve(), SVDGetSingularTriplet()
    266: @*/
    267: PetscErrorCode SVDGetConverged(SVD svd,PetscInt *nconv)
    268: {
    269:   PetscFunctionBegin;
    272:   SVDCheckSolved(svd,1);
    273:   *nconv = svd->nconv;
    274:   PetscFunctionReturn(PETSC_SUCCESS);
    275: }

    277: /*@C
    278:    SVDGetSingularTriplet - Gets the i-th triplet of the singular value decomposition
    279:    as computed by SVDSolve(). The solution consists in the singular value and its left
    280:    and right singular vectors.

    282:    Collective

    284:    Input Parameters:
    285: +  svd - singular value solver context
    286: -  i   - index of the solution

    288:    Output Parameters:
    289: +  sigma - singular value
    290: .  u     - left singular vector
    291: -  v     - right singular vector

    293:    Note:
    294:    Both u or v can be NULL if singular vectors are not required.
    295:    Otherwise, the caller must provide valid Vec objects, i.e.,
    296:    they must be created by the calling program with e.g. MatCreateVecs().

    298:    The index i should be a value between 0 and nconv-1 (see SVDGetConverged()).
    299:    Singular triplets are indexed according to the ordering criterion established
    300:    with SVDSetWhichSingularTriplets().

    302:    In the case of GSVD, the solution consists in three vectors u,v,x that are
    303:    returned as follows. Vector x is returned in the right singular vector
    304:    (argument v) and has length equal to the number of columns of A and B.
    305:    The other two vectors are returned stacked on top of each other [u;v] in
    306:    the left singular vector argument, with length equal to m+n (number of rows
    307:    of A plus number of rows of B).

    309:    Level: beginner

    311: .seealso: SVDSolve(), SVDGetConverged(), SVDSetWhichSingularTriplets()
    312: @*/
    313: PetscErrorCode SVDGetSingularTriplet(SVD svd,PetscInt i,PetscReal *sigma,Vec u,Vec v)
    314: {
    315:   PetscInt       M,N;
    316:   Vec            w;

    318:   PetscFunctionBegin;
    321:   SVDCheckSolved(svd,1);
    324:   PetscCheck(i>=0,PetscObjectComm((PetscObject)svd),PETSC_ERR_ARG_OUTOFRANGE,"The index cannot be negative");
    325:   PetscCheck(i<svd->nconv,PetscObjectComm((PetscObject)svd),PETSC_ERR_ARG_OUTOFRANGE,"The index can be nconv-1 at most, see SVDGetConverged()");
    326:   if (sigma) *sigma = svd->sigma[svd->perm[i]];
    327:   if (u || v) {
    328:     if (!svd->isgeneralized) {
    329:       PetscCall(MatGetSize(svd->OP,&M,&N));
    330:       if (M<N) { w = u; u = v; v = w; }
    331:     }
    332:     PetscCall(SVDComputeVectors(svd));
    333:     if (u) PetscCall(BVCopyVec(svd->U,svd->perm[i],u));
    334:     if (v) PetscCall(BVCopyVec(svd->V,svd->perm[i],v));
    335:   }
    336:   PetscFunctionReturn(PETSC_SUCCESS);
    337: }

    339: /*
    340:    SVDComputeResidualNorms_Standard - Computes the norms of the left and
    341:    right residuals associated with the i-th computed singular triplet.

    343:    Input Parameters:
    344:      sigma - singular value
    345:      u,v   - singular vectors
    346:      x,y   - two work vectors with the same dimensions as u,v
    347: */
    348: static PetscErrorCode SVDComputeResidualNorms_Standard(SVD svd,PetscReal sigma,Vec u,Vec v,Vec x,Vec y,PetscReal *norm1,PetscReal *norm2)
    349: {
    350:   PetscInt       M,N;

    352:   PetscFunctionBegin;
    353:   /* norm1 = ||A*v-sigma*u||_2 */
    354:   if (norm1) {
    355:     PetscCall(MatMult(svd->OP,v,x));
    356:     PetscCall(VecAXPY(x,-sigma,u));
    357:     PetscCall(VecNorm(x,NORM_2,norm1));
    358:   }
    359:   /* norm2 = ||A^T*u-sigma*v||_2 */
    360:   if (norm2) {
    361:     PetscCall(MatGetSize(svd->OP,&M,&N));
    362:     if (M<N) PetscCall(MatMult(svd->A,u,y));
    363:     else PetscCall(MatMult(svd->AT,u,y));
    364:     PetscCall(VecAXPY(y,-sigma,v));
    365:     PetscCall(VecNorm(y,NORM_2,norm2));
    366:   }
    367:   PetscFunctionReturn(PETSC_SUCCESS);
    368: }

    370: /*
    371:    SVDComputeResidualNorms_Generalized - In GSVD, compute the residual norms
    372:    norm1 = ||s^2*A'*u-c*B'*B*x||_2 and norm2 = ||c^2*B'*v-s*A'*A*x||_2.

    374:    Input Parameters:
    375:      sigma - singular value
    376:      uv    - left singular vectors [u;v]
    377:      x     - right singular vector
    378:      y,z   - two work vectors with the same dimension as x
    379: */
    380: static PetscErrorCode SVDComputeResidualNorms_Generalized(SVD svd,PetscReal sigma,Vec uv,Vec x,Vec y,Vec z,PetscReal *norm1,PetscReal *norm2)
    381: {
    382:   Vec            u,v,ax,bx,nest,aux[2];
    383:   PetscReal      c,s;

    385:   PetscFunctionBegin;
    386:   PetscCall(MatCreateVecs(svd->OP,NULL,&u));
    387:   PetscCall(MatCreateVecs(svd->OPb,NULL,&v));
    388:   aux[0] = u;
    389:   aux[1] = v;
    390:   PetscCall(VecCreateNest(PetscObjectComm((PetscObject)svd),2,NULL,aux,&nest));
    391:   PetscCall(VecCopy(uv,nest));

    393:   s = 1.0/PetscSqrtReal(1.0+sigma*sigma);
    394:   c = sigma*s;

    396:   /* norm1 = ||s^2*A'*u-c*B'*B*x||_2 */
    397:   if (norm1) {
    398:     PetscCall(VecDuplicate(v,&bx));
    399:     PetscCall(MatMultHermitianTranspose(svd->OP,u,z));
    400:     PetscCall(MatMult(svd->OPb,x,bx));
    401:     PetscCall(MatMultHermitianTranspose(svd->OPb,bx,y));
    402:     PetscCall(VecAXPBY(y,s*s,-c,z));
    403:     PetscCall(VecNorm(y,NORM_2,norm1));
    404:     PetscCall(VecDestroy(&bx));
    405:   }
    406:   /* norm2 = ||c^2*B'*v-s*A'*A*x||_2 */
    407:   if (norm2) {
    408:     PetscCall(VecDuplicate(u,&ax));
    409:     PetscCall(MatMultHermitianTranspose(svd->OPb,v,z));
    410:     PetscCall(MatMult(svd->OP,x,ax));
    411:     PetscCall(MatMultHermitianTranspose(svd->OP,ax,y));
    412:     PetscCall(VecAXPBY(y,c*c,-s,z));
    413:     PetscCall(VecNorm(y,NORM_2,norm2));
    414:     PetscCall(VecDestroy(&ax));
    415:   }

    417:   PetscCall(VecDestroy(&v));
    418:   PetscCall(VecDestroy(&u));
    419:   PetscCall(VecDestroy(&nest));
    420:   PetscFunctionReturn(PETSC_SUCCESS);
    421: }

    423: /*
    424:    SVDComputeResidualNorms_Hyperbolic - Computes the norms of the left and
    425:    right residuals associated with the i-th computed singular triplet.

    427:    Input Parameters:
    428:      sigma - singular value
    429:      sign  - corresponding element of the signature Omega2
    430:      u,v   - singular vectors
    431:      x,y,z - three work vectors with the same dimensions as u,v,u
    432: */
    433: static PetscErrorCode SVDComputeResidualNorms_Hyperbolic(SVD svd,PetscReal sigma,PetscReal sign,Vec u,Vec v,Vec x,Vec y,Vec z,PetscReal *norm1,PetscReal *norm2)
    434: {
    435:   PetscInt       M,N;

    437:   PetscFunctionBegin;
    438:   /* norm1 = ||A*v-sigma*u||_2 */
    439:   if (norm1) {
    440:     PetscCall(MatMult(svd->OP,v,x));
    441:     PetscCall(VecAXPY(x,-sigma,u));
    442:     PetscCall(VecNorm(x,NORM_2,norm1));
    443:   }
    444:   /* norm2 = ||A^T*Omega*u-sigma*sign*v||_2 */
    445:   if (norm2) {
    446:     PetscCall(MatGetSize(svd->OP,&M,&N));
    447:     PetscCall(VecPointwiseMult(z,u,svd->omega));
    448:     if (M<N) PetscCall(MatMult(svd->A,z,y));
    449:     else PetscCall(MatMult(svd->AT,z,y));
    450:     PetscCall(VecAXPY(y,-sigma*sign,v));
    451:     PetscCall(VecNorm(y,NORM_2,norm2));
    452:   }
    453:   PetscFunctionReturn(PETSC_SUCCESS);
    454: }

    456: /*@
    457:    SVDComputeError - Computes the error (based on the residual norm) associated
    458:    with the i-th singular triplet.

    460:    Collective

    462:    Input Parameters:
    463: +  svd  - the singular value solver context
    464: .  i    - the solution index
    465: -  type - the type of error to compute

    467:    Output Parameter:
    468: .  error - the error

    470:    Notes:
    471:    The error can be computed in various ways, all of them based on the residual
    472:    norm obtained as sqrt(n1^2+n2^2) with n1 = ||A*v-sigma*u||_2 and
    473:    n2 = ||A^T*u-sigma*v||_2, where sigma is the singular value, u is the left
    474:    singular vector and v is the right singular vector.

    476:    In the case of the GSVD, the two components of the residual norm are
    477:    n1 = ||s^2*A'*u-c*B'*B*x||_2 and n2 = ||c^2*B'*v-s*A'*A*x||_2, where [u;v]
    478:    are the left singular vectors and x is the right singular vector, with
    479:    sigma=c/s.

    481:    Level: beginner

    483: .seealso: SVDErrorType, SVDSolve()
    484: @*/
    485: PetscErrorCode SVDComputeError(SVD svd,PetscInt i,SVDErrorType type,PetscReal *error)
    486: {
    487:   PetscReal      sigma,norm1,norm2;
    488:   Vec            u=NULL,v=NULL,x=NULL,y=NULL,z=NULL;

    490:   PetscFunctionBegin;
    495:   SVDCheckSolved(svd,1);

    497:   /* allocate work vectors */
    498:   switch (svd->problem_type) {
    499:     case SVD_STANDARD:
    500:       PetscCall(SVDSetWorkVecs(svd,2,2));
    501:       u = svd->workl[0];
    502:       v = svd->workr[0];
    503:       x = svd->workl[1];
    504:       y = svd->workr[1];
    505:       break;
    506:     case SVD_GENERALIZED:
    507:       PetscCheck(type!=SVD_ERROR_RELATIVE,PetscObjectComm((PetscObject)svd),PETSC_ERR_SUP,"In GSVD the error should be either absolute or relative to the norms");
    508:       PetscCall(SVDSetWorkVecs(svd,1,3));
    509:       u = svd->workl[0];
    510:       v = svd->workr[0];
    511:       x = svd->workr[1];
    512:       y = svd->workr[2];
    513:       break;
    514:     case SVD_HYPERBOLIC:
    515:       PetscCall(SVDSetWorkVecs(svd,3,2));
    516:       u = svd->workl[0];
    517:       v = svd->workr[0];
    518:       x = svd->workl[1];
    519:       y = svd->workr[1];
    520:       z = svd->workl[2];
    521:       break;
    522:   }

    524:   /* compute residual norm and error */
    525:   PetscCall(SVDGetSingularTriplet(svd,i,&sigma,u,v));
    526:   switch (svd->problem_type) {
    527:     case SVD_STANDARD:
    528:       PetscCall(SVDComputeResidualNorms_Standard(svd,sigma,u,v,x,y,&norm1,&norm2));
    529:       break;
    530:     case SVD_GENERALIZED:
    531:       PetscCall(SVDComputeResidualNorms_Generalized(svd,sigma,u,v,x,y,&norm1,&norm2));
    532:       break;
    533:     case SVD_HYPERBOLIC:
    534:       PetscCall(SVDComputeResidualNorms_Hyperbolic(svd,sigma,svd->sign[svd->perm[i]],u,v,x,y,z,&norm1,&norm2));
    535:       break;
    536:   }
    537:   *error = SlepcAbs(norm1,norm2);
    538:   switch (type) {
    539:     case SVD_ERROR_ABSOLUTE:
    540:       break;
    541:     case SVD_ERROR_RELATIVE:
    542:       *error /= sigma;
    543:       break;
    544:     case SVD_ERROR_NORM:
    545:       if (!svd->nrma) PetscCall(MatNorm(svd->OP,NORM_INFINITY,&svd->nrma));
    546:       if (svd->isgeneralized && !svd->nrmb) PetscCall(MatNorm(svd->OPb,NORM_INFINITY,&svd->nrmb));
    547:       *error /= PetscMax(svd->nrma,svd->nrmb);
    548:       break;
    549:     default:
    550:       SETERRQ(PetscObjectComm((PetscObject)svd),PETSC_ERR_ARG_OUTOFRANGE,"Invalid error type");
    551:   }
    552:   PetscFunctionReturn(PETSC_SUCCESS);
    553: }