Actual source code: sinvert.c

      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:    Implements the shift-and-invert technique for eigenvalue problems
     12: */

     14: #include <slepc/private/stimpl.h>

     16: /*
     17:    Special STApply() for the BSE structured matrix

     19:        H = [ R  C; -C^H -R^T ].

     21:    Assumes that H is a MATNEST and x,y are VECNEST.

     23:    The bottom part of the input vector x2 is computed as
     24:    either conj(x1) or -conj(x1), where the sign is given by
     25:    s in the context SlepcMatStruct.
     26: */
     27: static PetscErrorCode STApply_Sinvert_BSE(ST st,Vec x,Vec y)
     28: {
     29:   Vec            x1,x2;
     30:   PetscContainer container;
     31:   SlepcMatStruct mctx;

     33:   PetscFunctionBegin;
     34:   PetscCall(PetscObjectQuery((PetscObject)st->A[0],"SlepcMatStruct",(PetscObject*)&container));
     35:   PetscCall(PetscContainerGetPointer(container,(void**)&mctx));
     36:   PetscCall(VecNestGetSubVec(x,0,&x1));
     37:   PetscCall(VecNestGetSubVec(x,1,&x2));

     39:   /* x2 = +/-conj(x1) */
     40:   PetscCall(VecCopy(x1,x2));
     41:   PetscCall(VecConjugate(x2));
     42:   if (mctx->s==-1.0) PetscCall(VecScale(x2,-1.0));

     44:   PetscCall(STApply_Generic(st,x,y));
     45:   PetscFunctionReturn(PETSC_SUCCESS);
     46: }

     48: static PetscErrorCode STBackTransform_Sinvert(ST st,PetscInt n,PetscScalar *eigr,PetscScalar *eigi)
     49: {
     50:   PetscInt    j;
     51: #if !defined(PETSC_USE_COMPLEX)
     52:   PetscScalar t;
     53: #endif

     55:   PetscFunctionBegin;
     56: #if !defined(PETSC_USE_COMPLEX)
     57:   for (j=0;j<n;j++) {
     58:     if (eigi[j] == 0) eigr[j] = 1.0 / eigr[j] + st->sigma;
     59:     else {
     60:       t = eigr[j] * eigr[j] + eigi[j] * eigi[j];
     61:       eigr[j] = eigr[j] / t + st->sigma;
     62:       eigi[j] = - eigi[j] / t;
     63:     }
     64:   }
     65: #else
     66:   for (j=0;j<n;j++) {
     67:     eigr[j] = 1.0 / eigr[j] + st->sigma;
     68:   }
     69: #endif
     70:   PetscFunctionReturn(PETSC_SUCCESS);
     71: }

     73: static PetscErrorCode STPostSolve_Sinvert(ST st)
     74: {
     75:   PetscFunctionBegin;
     76:   if (st->matmode == ST_MATMODE_INPLACE) {
     77:     if (st->nmat>1) PetscCall(MatAXPY(st->A[0],st->sigma,st->A[1],st->str));
     78:     else PetscCall(MatShift(st->A[0],st->sigma));
     79:     st->Astate[0] = ((PetscObject)st->A[0])->state;
     80:     st->state   = ST_STATE_INITIAL;
     81:     st->opready = PETSC_FALSE;
     82:   }
     83:   PetscFunctionReturn(PETSC_SUCCESS);
     84: }

     86: /*
     87:    Operator (sinvert):
     88:                Op               P         M
     89:    if nmat=1:  (A-sI)^-1        A-sI      NULL
     90:    if nmat=2:  (A-sB)^-1 B      A-sB      B
     91: */
     92: static PetscErrorCode STComputeOperator_Sinvert(ST st)
     93: {
     94:   PetscFunctionBegin;
     95:   /* if the user did not set the shift, use the target value */
     96:   if (!st->sigma_set) st->sigma = st->defsigma;
     97:   PetscCall(PetscObjectReference((PetscObject)st->A[1]));
     98:   PetscCall(MatDestroy(&st->T[0]));
     99:   st->T[0] = st->A[1];
    100:   PetscCall(STMatMAXPY_Private(st,-st->sigma,0.0,0,NULL,PetscNot(st->state==ST_STATE_UPDATED),PETSC_FALSE,&st->T[1]));
    101:   PetscCall(PetscObjectReference((PetscObject)st->T[1]));
    102:   PetscCall(MatDestroy(&st->P));
    103:   st->P = st->T[1];
    104:   st->M = (st->nmat>1)? st->T[0]: NULL;
    105:   if (st->Psplit) {  /* build custom preconditioner from the split matrices */
    106:     PetscCall(STMatMAXPY_Private(st,-st->sigma,0.0,0,NULL,PETSC_TRUE,PETSC_TRUE,&st->Pmat));
    107:   }
    108:   PetscFunctionReturn(PETSC_SUCCESS);
    109: }

    111: static PetscErrorCode STSetUp_Sinvert(ST st)
    112: {
    113:   PetscInt       k,nc,nmat=st->nmat,nsub;
    114:   PetscScalar    *coeffs=NULL;
    115:   PetscBool      islu;
    116:   KSP            *subksp;
    117:   PC             pc,pcsub;
    118:   Mat            A00;
    119:   MatType        type;
    120:   PetscBool      flg;
    121:   char           str[64];

    123:   PetscFunctionBegin;
    124:   if (nmat>1) PetscCall(STSetWorkVecs(st,1));
    125:   /* if the user did not set the shift, use the target value */
    126:   if (!st->sigma_set) st->sigma = st->defsigma;
    127:   if (nmat>2) {  /* set-up matrices for polynomial eigenproblems */
    128:     if (st->transform) {
    129:       nc = (nmat*(nmat+1))/2;
    130:       PetscCall(PetscMalloc1(nc,&coeffs));
    131:       /* Compute coeffs */
    132:       PetscCall(STCoeffs_Monomial(st,coeffs));
    133:       /* T[0] = A_n */
    134:       k = nmat-1;
    135:       PetscCall(PetscObjectReference((PetscObject)st->A[k]));
    136:       PetscCall(MatDestroy(&st->T[0]));
    137:       st->T[0] = st->A[k];
    138:       for (k=1;k<nmat;k++) PetscCall(STMatMAXPY_Private(st,nmat>2?st->sigma:-st->sigma,0.0,nmat-k-1,coeffs?coeffs+(k*(k+1))/2:NULL,PetscNot(st->state==ST_STATE_UPDATED),PETSC_FALSE,&st->T[k]));
    139:       PetscCall(PetscFree(coeffs));
    140:       PetscCall(PetscObjectReference((PetscObject)st->T[nmat-1]));
    141:       PetscCall(MatDestroy(&st->P));
    142:       st->P = st->T[nmat-1];
    143:       if (st->Psplit) {  /* build custom preconditioner from the split matrices */
    144:         PetscCall(STMatMAXPY_Private(st,st->sigma,0.0,0,coeffs?coeffs+((nmat-1)*nmat)/2:NULL,PETSC_TRUE,PETSC_TRUE,&st->Pmat));
    145:       }
    146:       PetscCall(ST_KSPSetOperators(st,st->P,st->Pmat?st->Pmat:st->P));
    147:     } else {
    148:       for (k=0;k<nmat;k++) {
    149:         PetscCall(PetscObjectReference((PetscObject)st->A[k]));
    150:         PetscCall(MatDestroy(&st->T[k]));
    151:         st->T[k] = st->A[k];
    152:       }
    153:     }
    154:   }
    155:   if (st->structured) {
    156:     /* ./ex55 -st_type sinvert -eps_target 0 -eps_target_magnitude
    157:               -st_ksp_type preonly -st_pc_type fieldsplit
    158:               -st_fieldsplit_0_ksp_type preonly -st_fieldsplit_0_pc_type lu
    159:               -st_fieldsplit_1_ksp_type preonly -st_fieldsplit_1_pc_type lu
    160:               -st_pc_fieldsplit_type schur -st_pc_fieldsplit_schur_fact_type full
    161:               -st_pc_fieldsplit_schur_precondition full */
    162:     PetscCall(KSPGetPC(st->ksp,&pc));
    163:     PetscCall(PetscObjectTypeCompare((PetscObject)pc,PCLU,&islu));
    164:     if (islu) {  /* assume PCLU means the user has not set any options */
    165:       PetscCall(KSPSetType(st->ksp,KSPPREONLY));
    166:       PetscCall(PCSetType(pc,PCFIELDSPLIT));
    167:       PetscCall(PCFieldSplitSetType(pc,PC_COMPOSITE_SCHUR));
    168:       PetscCall(PCFieldSplitSetSchurFactType(pc,PC_FIELDSPLIT_SCHUR_FACT_FULL));
    169:       PetscCall(PCFieldSplitSetSchurPre(pc,PC_FIELDSPLIT_SCHUR_PRE_FULL,NULL));
    170:       /* hack to set Mat type of Schur complement equal to A00, assumes default prefixes */
    171:       PetscCall(PetscOptionsHasName(((PetscObject)st)->options,((PetscObject)st)->prefix,"-st_fieldsplit_1_explicit_operator_mat_type",&flg));
    172:       if (!flg) {
    173:         PetscCall(MatNestGetSubMat(st->A[0],0,0,&A00));
    174:         PetscCall(MatGetType(A00,&type));
    175:         PetscCall(PetscSNPrintf(str,sizeof(str),"-%sst_fieldsplit_1_explicit_operator_mat_type %s",((PetscObject)st)->prefix?((PetscObject)st)->prefix:"",type));
    176:         PetscCall(PetscOptionsInsertString(((PetscObject)st)->options,str));
    177:       }
    178:       /* set preonly+lu on block solvers */
    179:       PetscCall(KSPSetUp(st->ksp));
    180:       PetscCall(PCFieldSplitGetSubKSP(pc,&nsub,&subksp));
    181:       PetscCall(KSPSetType(subksp[0],KSPPREONLY));
    182:       PetscCall(KSPGetPC(subksp[0],&pcsub));
    183:       PetscCall(PCSetType(pcsub,PCLU));
    184:       PetscCall(KSPSetType(subksp[1],KSPPREONLY));
    185:       PetscCall(KSPGetPC(subksp[1],&pcsub));
    186:       PetscCall(PCSetType(pcsub,PCLU));
    187:       PetscCall(PetscFree(subksp));
    188:     }
    189:     st->ops->apply = STApply_Sinvert_BSE;
    190:   } else {
    191:     if (st->P) PetscCall(KSPSetUp(st->ksp));
    192:   }
    193:   PetscFunctionReturn(PETSC_SUCCESS);
    194: }

    196: static PetscErrorCode STSetShift_Sinvert(ST st,PetscScalar newshift)
    197: {
    198:   PetscInt       nmat=PetscMax(st->nmat,2),k,nc;
    199:   PetscScalar    *coeffs=NULL;

    201:   PetscFunctionBegin;
    202:   if (st->transform) {
    203:     if (st->matmode == ST_MATMODE_COPY && nmat>2) {
    204:       nc = (nmat*(nmat+1))/2;
    205:       PetscCall(PetscMalloc1(nc,&coeffs));
    206:       /* Compute coeffs */
    207:       PetscCall(STCoeffs_Monomial(st,coeffs));
    208:     }
    209:     for (k=1;k<nmat;k++) PetscCall(STMatMAXPY_Private(st,nmat>2?newshift:-newshift,nmat>2?st->sigma:-st->sigma,nmat-k-1,coeffs?coeffs+(k*(k+1))/2:NULL,PETSC_FALSE,PETSC_FALSE,&st->T[k]));
    210:     if (st->matmode == ST_MATMODE_COPY && nmat>2) PetscCall(PetscFree(coeffs));
    211:     if (st->P!=st->T[nmat-1]) {
    212:       PetscCall(PetscObjectReference((PetscObject)st->T[nmat-1]));
    213:       PetscCall(MatDestroy(&st->P));
    214:       st->P = st->T[nmat-1];
    215:     }
    216:     if (st->Psplit) {  /* build custom preconditioner from the split matrices */
    217:       PetscCall(STMatMAXPY_Private(st,nmat>2?newshift:-newshift,nmat>2?st->sigma:-st->sigma,0,coeffs?coeffs+((nmat-1)*nmat)/2:NULL,PETSC_FALSE,PETSC_TRUE,&st->Pmat));
    218:     }
    219:   }
    220:   if (st->P) {
    221:     PetscCall(ST_KSPSetOperators(st,st->P,st->Pmat?st->Pmat:st->P));
    222:     PetscCall(KSPSetUp(st->ksp));
    223:   }
    224:   PetscFunctionReturn(PETSC_SUCCESS);
    225: }

    227: /*MC
    228:    STSINVERT - STSINVERT = "sinvert" - The shift-and-invert spectral
    229:    transformation.

    231:    Level: beginner

    233:    Notes:
    234:    The operator in this `ST` is $(A-\sigma B)^{-1}B$, where $A$ and $B$ are the
    235:    matrices set with `STSetMatrices()` and the shift $\sigma$ is given in
    236:    `STSetShift()`.

    238:    This is used to enhance convergence of eigenvalues in the neighborhood of
    239:    $\sigma$, at the cost of solving a system of linear equations at each
    240:    eigensolver step.

    242: .seealso: [](ch:st), `ST`, `STType`, `STSetType()`, `STSetMatrices()`, `STSetShift()`
    243: M*/

    245: SLEPC_EXTERN PetscErrorCode STCreate_Sinvert(ST st)
    246: {
    247:   PetscFunctionBegin;
    248:   st->usesksp = PETSC_TRUE;

    250:   st->ops->apply           = STApply_Generic;
    251:   st->ops->applytrans      = STApplyTranspose_Generic;
    252:   st->ops->applyhermtrans  = STApplyHermitianTranspose_Generic;
    253:   st->ops->backtransform   = STBackTransform_Sinvert;
    254:   st->ops->setshift        = STSetShift_Sinvert;
    255:   st->ops->getbilinearform = STGetBilinearForm_Default;
    256:   st->ops->setup           = STSetUp_Sinvert;
    257:   st->ops->computeoperator = STComputeOperator_Sinvert;
    258:   st->ops->postsolve       = STPostSolve_Sinvert;
    259:   st->ops->checknullspace  = STCheckNullSpace_Default;
    260:   st->ops->setdefaultksp   = STSetDefaultKSP_Default;
    261:   PetscFunctionReturn(PETSC_SUCCESS);
    262: }