Date: 2025-03-29T05:58:29+00:00

Actual source code: sr1.c

      1: #include <../src/ksp/ksp/utils/lmvm/lmvm.h>

      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: /*
     16:   The solution method is adapted from Algorithm 8 of Erway and Marcia
     17:   "On Solving Large-Scale Limited-Memory Quasi-Newton Equations"
     18:   (https://arxiv.org/abs/1510.06378).

     20:   Q[i] = S[i] - (B_i)^{-1}*Y[i] terms are computed ahead of time whenever
     21:   the matrix is updated with a new (S[i], Y[i]) pair. This allows
     22:   repeated calls of MatMult inside KSP solvers without unnecessarily
     23:   recomputing Q[i] terms in expensive nested-loops.

     25:   dX <- J0^{-1} * F
     26:   for i = 0,1,2,...,k
     27:     # Q[i] = S[i] - (B_i)^{-1}*Y[i]
     28:     zeta = (Q[i]^T F) / (Q[i]^T Y[i])
     29:     dX <- dX + (zeta * Q[i])
     30:   end
     31: */
     32: static PetscErrorCode MatSolve_LMVMSR1(Mat B, Vec F, Vec dX)
     33: {
     34:   Mat_LMVM   *lmvm = (Mat_LMVM *)B->data;
     35:   Mat_LSR1   *lsr1 = (Mat_LSR1 *)lmvm->ctx;
     36:   PetscInt    i, j;
     37:   PetscScalar qjtyi, qtf, ytq;

     39:   PetscFunctionBegin;
     40:   VecCheckSameSize(F, 2, dX, 3);
     41:   VecCheckMatCompatible(B, dX, 3, F, 2);

     43:   if (lsr1->needQ) {
     44:     /* Pre-compute (Q[i] = S[i] - (B_i)^{-1} * Y[i]) and (Y[i]^T Q[i]) */
     45:     for (i = 0; i <= lmvm->k; ++i) {
     46:       PetscCall(MatLMVMApplyJ0Inv(B, lmvm->Y[i], lsr1->Q[i]));
     47:       PetscCall(VecAYPX(lsr1->Q[i], -1.0, lmvm->S[i]));
     48:       for (j = 0; j <= i - 1; ++j) {
     49:         PetscCall(VecDot(lsr1->Q[j], lmvm->Y[i], &qjtyi));
     50:         PetscCall(VecAXPY(lsr1->Q[i], -PetscRealPart(qjtyi) / lsr1->ytq[j], lsr1->Q[j]));
     51:       }
     52:       PetscCall(VecDot(lmvm->Y[i], lsr1->Q[i], &ytq));
     53:       lsr1->ytq[i] = PetscRealPart(ytq);
     54:     }
     55:     lsr1->needQ = PETSC_FALSE;
     56:   }

     58:   /* Invert the initial Jacobian onto F (or apply scaling) */
     59:   PetscCall(MatLMVMApplyJ0Inv(B, F, dX));
     60:   /* Start outer loop */
     61:   for (i = 0; i <= lmvm->k; ++i) {
     62:     PetscCall(VecDot(lsr1->Q[i], F, &qtf));
     63:     PetscCall(VecAXPY(dX, PetscRealPart(qtf) / lsr1->ytq[i], lsr1->Q[i]));
     64:   }
     65:   PetscFunctionReturn(PETSC_SUCCESS);
     66: }

     68: /*
     69:   The forward product is the matrix-free implementation of
     70:   Equation (6.24) in Nocedal and Wright "Numerical Optimization"
     71:   2nd edition, pg 144.

     73:   Note that the structure of the forward product is identical to
     74:   the solution, with S and Y exchanging roles.

     76:   P[i] = Y[i] - (B_i)*S[i] terms are computed ahead of time whenever
     77:   the matrix is updated with a new (S[i], Y[i]) pair. This allows
     78:   repeated calls of MatMult inside KSP solvers without unnecessarily
     79:   recomputing P[i] terms in expensive nested-loops.

     81:   Z <- J0 * X
     82:   for i = 0,1,2,...,k
     83:     # P[i] = Y[i] - (B_i)*S[i]
     84:     zeta = (P[i]^T X) / (P[i]^T S[i])
     85:     Z <- Z + (zeta * P[i])
     86:   end
     87: */
     88: static PetscErrorCode MatMult_LMVMSR1(Mat B, Vec X, Vec Z)
     89: {
     90:   Mat_LMVM   *lmvm = (Mat_LMVM *)B->data;
     91:   Mat_LSR1   *lsr1 = (Mat_LSR1 *)lmvm->ctx;
     92:   PetscInt    i, j;
     93:   PetscScalar pjtsi, ptx, stp;

     95:   PetscFunctionBegin;
     96:   VecCheckSameSize(X, 2, Z, 3);
     97:   VecCheckMatCompatible(B, X, 2, Z, 3);

     99:   if (lsr1->needP) {
    100:     /* Pre-compute (P[i] = Y[i] - (B_i) * S[i]) and (S[i]^T P[i]) */
    101:     for (i = 0; i <= lmvm->k; ++i) {
    102:       PetscCall(MatLMVMApplyJ0Fwd(B, lmvm->S[i], lsr1->P[i]));
    103:       PetscCall(VecAYPX(lsr1->P[i], -1.0, lmvm->Y[i]));
    104:       for (j = 0; j <= i - 1; ++j) {
    105:         PetscCall(VecDot(lsr1->P[j], lmvm->S[i], &pjtsi));
    106:         PetscCall(VecAXPY(lsr1->P[i], -PetscRealPart(pjtsi) / lsr1->stp[j], lsr1->P[j]));
    107:       }
    108:       PetscCall(VecDot(lmvm->S[i], lsr1->P[i], &stp));
    109:       lsr1->stp[i] = PetscRealPart(stp);
    110:     }
    111:     lsr1->needP = PETSC_FALSE;
    112:   }

    114:   PetscCall(MatLMVMApplyJ0Fwd(B, X, Z));
    115:   for (i = 0; i <= lmvm->k; ++i) {
    116:     PetscCall(VecDot(lsr1->P[i], X, &ptx));
    117:     PetscCall(VecAXPY(Z, PetscRealPart(ptx) / lsr1->stp[i], lsr1->P[i]));
    118:   }
    119:   PetscFunctionReturn(PETSC_SUCCESS);
    120: }

    122: static PetscErrorCode MatUpdate_LMVMSR1(Mat B, Vec X, Vec F)
    123: {
    124:   Mat_LMVM   *lmvm = (Mat_LMVM *)B->data;
    125:   Mat_LSR1   *lsr1 = (Mat_LSR1 *)lmvm->ctx;
    126:   PetscReal   snorm, pnorm;
    127:   PetscScalar sktw;

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

    158: static PetscErrorCode MatCopy_LMVMSR1(Mat B, Mat M, MatStructure str)
    159: {
    160:   Mat_LMVM *bdata = (Mat_LMVM *)B->data;
    161:   Mat_LSR1 *bctx  = (Mat_LSR1 *)bdata->ctx;
    162:   Mat_LMVM *mdata = (Mat_LMVM *)M->data;
    163:   Mat_LSR1 *mctx  = (Mat_LSR1 *)mdata->ctx;
    164:   PetscInt  i;

    166:   PetscFunctionBegin;
    167:   mctx->needP = bctx->needP;
    168:   mctx->needQ = bctx->needQ;
    169:   for (i = 0; i <= bdata->k; ++i) {
    170:     mctx->stp[i] = bctx->stp[i];
    171:     mctx->ytq[i] = bctx->ytq[i];
    172:     PetscCall(VecCopy(bctx->P[i], mctx->P[i]));
    173:     PetscCall(VecCopy(bctx->Q[i], mctx->Q[i]));
    174:   }
    175:   PetscFunctionReturn(PETSC_SUCCESS);
    176: }

    178: static PetscErrorCode MatReset_LMVMSR1(Mat B, PetscBool destructive)
    179: {
    180:   Mat_LMVM *lmvm = (Mat_LMVM *)B->data;
    181:   Mat_LSR1 *lsr1 = (Mat_LSR1 *)lmvm->ctx;

    183:   PetscFunctionBegin;
    184:   lsr1->needP = lsr1->needQ = PETSC_TRUE;
    185:   if (destructive && lsr1->allocated) {
    186:     PetscCall(VecDestroy(&lsr1->work));
    187:     PetscCall(PetscFree2(lsr1->stp, lsr1->ytq));
    188:     PetscCall(VecDestroyVecs(lmvm->m, &lsr1->P));
    189:     PetscCall(VecDestroyVecs(lmvm->m, &lsr1->Q));
    190:     lsr1->allocated = PETSC_FALSE;
    191:   }
    192:   PetscCall(MatReset_LMVM(B, destructive));
    193:   PetscFunctionReturn(PETSC_SUCCESS);
    194: }

    196: static PetscErrorCode MatAllocate_LMVMSR1(Mat B, Vec X, Vec F)
    197: {
    198:   Mat_LMVM *lmvm = (Mat_LMVM *)B->data;
    199:   Mat_LSR1 *lsr1 = (Mat_LSR1 *)lmvm->ctx;

    201:   PetscFunctionBegin;
    202:   PetscCall(MatAllocate_LMVM(B, X, F));
    203:   if (!lsr1->allocated) {
    204:     PetscCall(VecDuplicate(X, &lsr1->work));
    205:     PetscCall(PetscMalloc2(lmvm->m, &lsr1->stp, lmvm->m, &lsr1->ytq));
    206:     if (lmvm->m > 0) {
    207:       PetscCall(VecDuplicateVecs(X, lmvm->m, &lsr1->P));
    208:       PetscCall(VecDuplicateVecs(X, lmvm->m, &lsr1->Q));
    209:     }
    210:     lsr1->allocated = PETSC_TRUE;
    211:   }
    212:   PetscFunctionReturn(PETSC_SUCCESS);
    213: }

    215: static PetscErrorCode MatDestroy_LMVMSR1(Mat B)
    216: {
    217:   Mat_LMVM *lmvm = (Mat_LMVM *)B->data;
    218:   Mat_LSR1 *lsr1 = (Mat_LSR1 *)lmvm->ctx;

    220:   PetscFunctionBegin;
    221:   if (lsr1->allocated) {
    222:     PetscCall(VecDestroy(&lsr1->work));
    223:     PetscCall(PetscFree2(lsr1->stp, lsr1->ytq));
    224:     PetscCall(VecDestroyVecs(lmvm->m, &lsr1->P));
    225:     PetscCall(VecDestroyVecs(lmvm->m, &lsr1->Q));
    226:     lsr1->allocated = PETSC_FALSE;
    227:   }
    228:   PetscCall(PetscFree(lmvm->ctx));
    229:   PetscCall(MatDestroy_LMVM(B));
    230:   PetscFunctionReturn(PETSC_SUCCESS);
    231: }

    233: static PetscErrorCode MatSetUp_LMVMSR1(Mat B)
    234: {
    235:   Mat_LMVM *lmvm = (Mat_LMVM *)B->data;
    236:   Mat_LSR1 *lsr1 = (Mat_LSR1 *)lmvm->ctx;

    238:   PetscFunctionBegin;
    239:   PetscCall(MatSetUp_LMVM(B));
    240:   if (!lsr1->allocated && lmvm->m > 0) {
    241:     PetscCall(VecDuplicate(lmvm->Xprev, &lsr1->work));
    242:     PetscCall(PetscMalloc2(lmvm->m, &lsr1->stp, lmvm->m, &lsr1->ytq));
    243:     if (lmvm->m > 0) {
    244:       PetscCall(VecDuplicateVecs(lmvm->Xprev, lmvm->m, &lsr1->P));
    245:       PetscCall(VecDuplicateVecs(lmvm->Xprev, lmvm->m, &lsr1->Q));
    246:     }
    247:     lsr1->allocated = PETSC_TRUE;
    248:   }
    249:   PetscFunctionReturn(PETSC_SUCCESS);
    250: }

    252: PetscErrorCode MatCreate_LMVMSR1(Mat B)
    253: {
    254:   Mat_LMVM *lmvm;
    255:   Mat_LSR1 *lsr1;

    257:   PetscFunctionBegin;
    258:   PetscCall(MatCreate_LMVM(B));
    259:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATLMVMSR1));
    260:   PetscCall(MatSetOption(B, MAT_SYMMETRIC, PETSC_TRUE));
    261:   B->ops->setup   = MatSetUp_LMVMSR1;
    262:   B->ops->destroy = MatDestroy_LMVMSR1;

    264:   lmvm                = (Mat_LMVM *)B->data;
    265:   lmvm->square        = PETSC_TRUE;
    266:   lmvm->ops->allocate = MatAllocate_LMVMSR1;
    267:   lmvm->ops->reset    = MatReset_LMVMSR1;
    268:   lmvm->ops->update   = MatUpdate_LMVMSR1;
    269:   lmvm->ops->mult     = MatMult_LMVMSR1;
    270:   lmvm->ops->copy     = MatCopy_LMVMSR1;
    271:   lmvm->ops->solve    = MatSolve_LMVMSR1;

    273:   PetscCall(PetscNew(&lsr1));
    274:   lmvm->ctx       = (void *)lsr1;
    275:   lsr1->allocated = PETSC_FALSE;
    276:   lsr1->needP = lsr1->needQ = PETSC_TRUE;
    277:   PetscFunctionReturn(PETSC_SUCCESS);
    278: }

    280: /*@
    281:   MatCreateLMVMSR1 - Creates a limited-memory Symmetric-Rank-1 approximation
    282:   matrix used for a Jacobian. L-SR1 is symmetric by construction, but is not
    283:   guaranteed to be positive-definite.

    285:   To use the L-SR1 matrix with other vector types, the matrix must be
    286:   created using `MatCreate()` and `MatSetType()`, followed by `MatLMVMAllocate()`.
    287:   This ensures that the internal storage and work vectors are duplicated from the
    288:   correct type of vector.

    290:   Collective

    292:   Input Parameters:
    293: + comm - MPI communicator
    294: . n    - number of local rows for storage vectors
    295: - N    - global size of the storage vectors

    297:   Output Parameter:
    298: . B - the matrix

    300:   Level: intermediate

    302:   Note:
    303:   It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`
    304:   paradigm instead of this routine directly.

    306: .seealso: [](ch_ksp), `MatCreate()`, `MATLMVM`, `MATLMVMSR1`, `MatCreateLMVMBFGS()`, `MatCreateLMVMDFP()`,
    307:           `MatCreateLMVMBrdn()`, `MatCreateLMVMBadBrdn()`, `MatCreateLMVMSymBrdn()`
    308: @*/
    309: PetscErrorCode MatCreateLMVMSR1(MPI_Comm comm, PetscInt n, PetscInt N, Mat *B)
    310: {
    311:   PetscFunctionBegin;
    312:   PetscCall(KSPInitializePackage());
    313:   PetscCall(MatCreate(comm, B));
    314:   PetscCall(MatSetSizes(*B, n, n, N, N));
    315:   PetscCall(MatSetType(*B, MATLMVMSR1));
    316:   PetscCall(MatSetUp(*B));
    317:   PetscFunctionReturn(PETSC_SUCCESS);
    318: }