Actual source code: sr1.c
petsc-3.10.5 2019-03-28
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      1: #include <../src/ksp/ksp/utils/lmvm/lmvm.h> /*I "petscksp.h" I*/

      3: /*
      4:   Limited-memory Symmetric-Rank-1 method for approximating both
      5:   the forward product and inverse application of a Jacobian.
      6: */

      8: typedef struct {
      9:   Vec *P, *Q;
     10:   Vec work;
     11:   PetscBool allocated, needP, needQ;
     12:   PetscReal *stp, *ytq;
     13: } Mat_LSR1;

     15: /*------------------------------------------------------------*/

     17: /*
     18:   The solution method is adapted from Algorithm 8 of Erway and Marcia
     19:   "On Solving Large-Scale Limited-Memory Quasi-Newton Equations"
     20:   (https://arxiv.org/abs/1510.06378).
     21:
     22:   Q[i] = S[i] - (B_i)^{-1}*Y[i] terms are computed ahead of time whenever
     23:   the matrix is updated with a new (S[i], Y[i]) pair. This allows
     24:   repeated calls of MatMult inside KSP solvers without unnecessarily
     25:   recomputing Q[i] terms in expensive nested-loops.

     27:   dX <- J0^{-1} * F
     28:   for i = 0,1,2,...,k
     29:     # Q[i] = S[i] - (B_i)^{-1}*Y[i]
     30:     zeta = (Q[i]^T F) / (Q[i]^T Y[i])
     31:     dX <- dX + (zeta * Q[i])
     32:   end
     33: */
     34: static PetscErrorCode MatSolve_LMVMSR1(Mat B, Vec F, Vec dX)
     35: {
     36:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
     37:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
     38:   PetscErrorCode    ierr;
     39:   PetscInt          i, j;
     40:   PetscScalar       qjtyi, qtf, ytq;
     41:
     43:   VecCheckSameSize(F, 2, dX, 3);
     44:   VecCheckMatCompatible(B, dX, 3, F, 2);
     45:
     46:   if (lsr1->needQ) {
     47:     /* Pre-compute (Q[i] = S[i] - (B_i)^{-1} * Y[i]) and (Y[i]^T Q[i]) */
     48:     for (i = 0; i <= lmvm->k; ++i) {
     49:       MatLMVMApplyJ0Inv(B, lmvm->Y[i], lsr1->Q[i]);
     50:       VecAYPX(lsr1->Q[i], -1.0, lmvm->S[i]);
     51:       for (j = 0; j <= i-1; ++j) {
     52:         VecDot(lsr1->Q[j], lmvm->Y[i], &qjtyi);
     53:         VecAXPY(lsr1->Q[i], -PetscRealPart(qjtyi)/lsr1->ytq[j], lsr1->Q[j]);
     54:       }
     55:       VecDot(lmvm->Y[i], lsr1->Q[i], &ytq);
     56:       lsr1->ytq[i] = PetscRealPart(ytq);
     57:     }
     58:     lsr1->needQ = PETSC_FALSE;
     59:   }
     60:
     61:   /* Invert the initial Jacobian onto F (or apply scaling) */
     62:   MatLMVMApplyJ0Inv(B, F, dX);
     63:   /* Start outer loop */
     64:   for (i = 0; i <= lmvm->k; ++i) {
     65:     VecDot(lsr1->Q[i], F, &qtf);
     66:     VecAXPY(dX, PetscRealPart(qtf)/lsr1->ytq[i], lsr1->Q[i]);
     67:   }
     68:   return(0);
     69: }

     71: /*------------------------------------------------------------*/

     73: /*
     74:   The forward product is the matrix-free implementation of
     75:   Equation (6.24) in Nocedal and Wright "Numerical Optimization"
     76:   2nd edition, pg 144.
     77:
     78:   Note that the structure of the forward product is identical to
     79:   the solution, with S and Y exchanging roles.
     80:
     81:   P[i] = Y[i] - (B_i)*S[i] terms are computed ahead of time whenever
     82:   the matrix is updated with a new (S[i], Y[i]) pair. This allows
     83:   repeated calls of MatMult inside KSP solvers without unnecessarily
     84:   recomputing P[i] terms in expensive nested-loops.

     86:   Z <- J0 * X
     87:   for i = 0,1,2,...,k
     88:     # P[i] = Y[i] - (B_i)*S[i]
     89:     zeta = (P[i]^T X) / (P[i]^T S[i])
     90:     Z <- Z + (zeta * P[i])
     91:   end
     92: */
     93: static PetscErrorCode MatMult_LMVMSR1(Mat B, Vec X, Vec Z)
     94: {
     95:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
     96:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
     97:   PetscErrorCode    ierr;
     98:   PetscInt          i, j;
     99:   PetscScalar       pjtsi, ptx, stp;
    100:
    102:   VecCheckSameSize(X, 2, Z, 3);
    103:   VecCheckMatCompatible(B, X, 2, Z, 3);
    104:
    105:   if (lsr1->needP) {
    106:     /* Pre-compute (P[i] = Y[i] - (B_i) * S[i]) and (S[i]^T P[i]) */
    107:     for (i = 0; i <= lmvm->k; ++i) {
    108:       MatLMVMApplyJ0Fwd(B, lmvm->S[i], lsr1->P[i]);
    109:       VecAYPX(lsr1->P[i], -1.0, lmvm->Y[i]);
    110:       for (j = 0; j <= i-1; ++j) {
    111:         VecDot(lsr1->P[j], lmvm->S[i], &pjtsi);
    112:         VecAXPY(lsr1->P[i], -PetscRealPart(pjtsi)/lsr1->stp[j], lsr1->P[j]);
    113:       }
    114:       VecDot(lmvm->S[i], lsr1->P[i], &stp);
    115:       lsr1->stp[i] = PetscRealPart(stp);
    116:     }
    117:     lsr1->needP = PETSC_FALSE;
    118:   }
    119:
    120:   MatLMVMApplyJ0Fwd(B, X, Z);
    121:   for (i = 0; i <= lmvm->k; ++i) {
    122:     VecDot(lsr1->P[i], X, &ptx);
    123:     VecAXPY(Z, PetscRealPart(ptx)/lsr1->stp[i], lsr1->P[i]);
    124:   }
    125:   return(0);
    126: }

    128: /*------------------------------------------------------------*/

    130: static PetscErrorCode MatUpdate_LMVMSR1(Mat B, Vec X, Vec F)
    131: {
    132:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
    133:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
    134:   PetscErrorCode    ierr;
    135:   PetscReal         snorm, pnorm;
    136:   PetscScalar       sktw;

    139:   if (!lmvm->m) return(0);
    140:   if (lmvm->prev_set) {
    141:     /* Compute the new (S = X - Xprev) and (Y = F - Fprev) vectors */
    142:     VecAYPX(lmvm->Xprev, -1.0, X);
    143:     VecAYPX(lmvm->Fprev, -1.0, F);
    144:     /* See if the updates can be accepted
    145:        NOTE: This tests abs(S[k]^T (Y[k] - B_k*S[k])) >= eps * norm(S[k]) * norm(Y[k] - B_k*S[k]) */
    146:     MatMult(B, lmvm->Xprev, lsr1->work);
    147:     VecAYPX(lsr1->work, -1.0, lmvm->Fprev);
    148:     VecDot(lmvm->Xprev, lsr1->work, &sktw);
    149:     VecNorm(lmvm->Xprev, NORM_2, &snorm);
    150:     VecNorm(lsr1->work, NORM_2, &pnorm);
    151:     if (PetscAbsReal(PetscRealPart(sktw)) >= lmvm->eps * snorm * pnorm) {
    152:       /* Update is good, accept it */
    153:       lsr1->needP = lsr1->needQ = PETSC_TRUE;
    154:       MatUpdateKernel_LMVM(B, lmvm->Xprev, lmvm->Fprev);
    155:     } else {
    156:       /* Update is bad, skip it */
    157:       ++lmvm->nrejects;
    158:     }
    159:   }
    160:   /* Save the solution and function to be used in the next update */
    161:   VecCopy(X, lmvm->Xprev);
    162:   VecCopy(F, lmvm->Fprev);
    163:   lmvm->prev_set = PETSC_TRUE;
    164:   return(0);
    165: }

    167: /*------------------------------------------------------------*/

    169: static PetscErrorCode MatCopy_LMVMSR1(Mat B, Mat M, MatStructure str)
    170: {
    171:   Mat_LMVM          *bdata = (Mat_LMVM*)B->data;
    172:   Mat_LSR1          *bctx = (Mat_LSR1*)bdata->ctx;
    173:   Mat_LMVM          *mdata = (Mat_LMVM*)M->data;
    174:   Mat_LSR1          *mctx = (Mat_LSR1*)mdata->ctx;
    175:   PetscErrorCode    ierr;
    176:   PetscInt          i;

    179:   mctx->needP = bctx->needP;
    180:   mctx->needQ = bctx->needQ;
    181:   for (i=0; i<=bdata->k; ++i) {
    182:     mctx->stp[i] = bctx->stp[i];
    183:     mctx->ytq[i] = bctx->ytq[i];
    184:     VecCopy(bctx->P[i], mctx->P[i]);
    185:     VecCopy(bctx->Q[i], mctx->Q[i]);
    186:   }
    187:   return(0);
    188: }

    190: /*------------------------------------------------------------*/

    192: static PetscErrorCode MatReset_LMVMSR1(Mat B, PetscBool destructive)
    193: {
    194:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
    195:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
    196:   PetscErrorCode    ierr;
    197:
    199:   lsr1->needP = lsr1->needQ = PETSC_TRUE;
    200:   if (destructive && lsr1->allocated) {
    201:     VecDestroy(&lsr1->work);
    202:     PetscFree2(lsr1->stp, lsr1->ytq);
    203:     VecDestroyVecs(lmvm->m, &lsr1->P);
    204:     VecDestroyVecs(lmvm->m, &lsr1->Q);
    205:     lsr1->allocated = PETSC_FALSE;
    206:   }
    207:   MatReset_LMVM(B, destructive);
    208:   return(0);
    209: }

    211: /*------------------------------------------------------------*/

    213: static PetscErrorCode MatAllocate_LMVMSR1(Mat B, Vec X, Vec F)
    214: {
    215:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
    216:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
    217:   PetscErrorCode    ierr;
    218:
    220:   MatAllocate_LMVM(B, X, F);
    221:   if (!lsr1->allocated) {
    222:     VecDuplicate(X, &lsr1->work);
    223:     PetscMalloc2(lmvm->m, &lsr1->stp, lmvm->m, &lsr1->ytq);
    224:     if (lmvm->m > 0) {
    225:       VecDuplicateVecs(X, lmvm->m, &lsr1->P);
    226:       VecDuplicateVecs(X, lmvm->m, &lsr1->Q);
    227:     }
    228:     lsr1->allocated = PETSC_TRUE;
    229:   }
    230:   return(0);
    231: }

    233: /*------------------------------------------------------------*/

    235: static PetscErrorCode MatDestroy_LMVMSR1(Mat B)
    236: {
    237:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
    238:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
    239:   PetscErrorCode    ierr;

    242:   if (lsr1->allocated) {
    243:     VecDestroy(&lsr1->work);
    244:     PetscFree2(lsr1->stp, lsr1->ytq);
    245:     VecDestroyVecs(lmvm->m, &lsr1->P);
    246:     VecDestroyVecs(lmvm->m, &lsr1->Q);
    247:     lsr1->allocated = PETSC_FALSE;
    248:   }
    249:   PetscFree(lmvm->ctx);
    250:   MatDestroy_LMVM(B);
    251:   return(0);
    252: }

    254: /*------------------------------------------------------------*/

    256: static PetscErrorCode MatSetUp_LMVMSR1(Mat B)
    257: {
    258:   Mat_LMVM          *lmvm = (Mat_LMVM*)B->data;
    259:   Mat_LSR1          *lsr1 = (Mat_LSR1*)lmvm->ctx;
    260:   PetscErrorCode    ierr;
    261:
    263:   MatSetUp_LMVM(B);
    264:   if (!lsr1->allocated && lmvm->m > 0) {
    265:     VecDuplicate(lmvm->Xprev, &lsr1->work);
    266:     PetscMalloc2(lmvm->m, &lsr1->stp, lmvm->m, &lsr1->ytq);
    267:     if (lmvm->m > 0) {
    268:       VecDuplicateVecs(lmvm->Xprev, lmvm->m, &lsr1->P);
    269:       VecDuplicateVecs(lmvm->Xprev, lmvm->m, &lsr1->Q);
    270:     }
    271:     lsr1->allocated = PETSC_TRUE;
    272:   }
    273:   return(0);
    274: }

    276: /*------------------------------------------------------------*/

    278: PetscErrorCode MatCreate_LMVMSR1(Mat B)
    279: {
    280:   Mat_LMVM          *lmvm;
    281:   Mat_LSR1          *lsr1;
    282:   PetscErrorCode    ierr;

    285:   MatCreate_LMVM(B);
    286:   PetscObjectChangeTypeName((PetscObject)B, MATLMVMSR1);
    287:   MatSetOption(B, MAT_SYMMETRIC, PETSC_TRUE);
    288:   B->ops->setup = MatSetUp_LMVMSR1;
    289:   B->ops->destroy = MatDestroy_LMVMSR1;
    290:   B->ops->solve = MatSolve_LMVMSR1;
    291:
    292:   lmvm = (Mat_LMVM*)B->data;
    293:   lmvm->square = PETSC_TRUE;
    294:   lmvm->ops->allocate = MatAllocate_LMVMSR1;
    295:   lmvm->ops->reset = MatReset_LMVMSR1;
    296:   lmvm->ops->update = MatUpdate_LMVMSR1;
    297:   lmvm->ops->mult = MatMult_LMVMSR1;
    298:   lmvm->ops->copy = MatCopy_LMVMSR1;
    299:
    300:   PetscNewLog(B, &lsr1);
    301:   lmvm->ctx = (void*)lsr1;
    302:   lsr1->allocated = PETSC_FALSE;
    303:   lsr1->needP = lsr1->needQ = PETSC_TRUE;
    304:   return(0);
    305: }

    307: /*------------------------------------------------------------*/

    309: /*@
    310:    MatCreateLMVMSR1 - Creates a limited-memory Symmetric-Rank-1 approximation
    311:    matrix used for a Jacobian. L-SR1 is symmetric by construction, but is not
    312:    guaranteed to be positive-definite.
    313:
    314:    The provided local and global sizes must match the solution and function vectors
    315:    used with MatLMVMUpdate() and MatSolve(). The resulting L-SR1 matrix will have
    316:    storage vectors allocated with VecCreateSeq() in serial and VecCreateMPI() in
    317:    parallel. To use the L-SR1 matrix with other vector types, the matrix must be
    318:    created using MatCreate() and MatSetType(), followed by MatLMVMAllocate().
    319:    This ensures that the internal storage and work vectors are duplicated from the
    320:    correct type of vector.

    322:    Collective on MPI_Comm

    324:    Input Parameters:
    325: +  comm - MPI communicator, set to PETSC_COMM_SELF
    326: .  n - number of local rows for storage vectors
    327: -  N - global size of the storage vectors

    329:    Output Parameter:
    330: .  B - the matrix

    332:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions()
    333:    paradigm instead of this routine directly.

    335:    Options Database Keys:
    336: .   -mat_lmvm_num_vecs - maximum number of correction vectors (i.e.: updates) stored

    338:    Level: intermediate

    340: .seealso: MatCreate(), MATLMVM, MATLMVMSR1, MatCreateLMVMBFGS(), MatCreateLMVMDFP(),
    341:           MatCreateLMVMBrdn(), MatCreateLMVMBadBrdn(), MatCreateLMVMSymBrdn()
    342: @*/
    343: PetscErrorCode MatCreateLMVMSR1(MPI_Comm comm, PetscInt n, PetscInt N, Mat *B)
    344: {
    345:   PetscErrorCode    ierr;
    346:
    348:   MatCreate(comm, B);
    349:   MatSetSizes(*B, n, n, N, N);
    350:   MatSetType(*B, MATLMVMSR1);
    351:   MatSetUp(*B);
    352:   return(0);
    353: }