Date: 2026-03-31T05:46:33+00:00

Actual source code: admm.c

      1: #include <../src/tao/constrained/impls/admm/admm.h>
      2: #include <petsctao.h>
      3: #include <petsc/private/petscimpl.h>

      5: /* Updates terminating criteria
      6:  *
      7:  * 1  ||r_k|| = ||Ax+Bz-c|| =< catol_admm* max{||Ax||,||Bz||,||c||}
      8:  *
      9:  * 2. Updates dual residual, d_k
     10:  *
     11:  * 3. ||d_k|| = ||mu*A^T*B(z_k-z_{k-1})|| =< gatol_admm * ||A^Ty||   */

     13: static PetscBool  cited      = PETSC_FALSE;
     14: static const char citation[] = "@misc{xu2017adaptive,\n"
     15:                                "   title={Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation},\n"
     16:                                "   author={Zheng Xu and Mario A. T. Figueiredo and Xiaoming Yuan and Christoph Studer and Tom Goldstein},\n"
     17:                                "   year={2017},\n"
     18:                                "   eprint={1704.02712},\n"
     19:                                "   archivePrefix={arXiv},\n"
     20:                                "   primaryClass={cs.CV}\n"
     21:                                "}  \n";

     23: const char *const TaoADMMRegularizerTypes[] = {"REGULARIZER_USER", "REGULARIZER_SOFT_THRESH", "TaoADMMRegularizerType", "TAO_ADMM_", NULL};
     24: const char *const TaoADMMUpdateTypes[]      = {"UPDATE_BASIC", "UPDATE_ADAPTIVE", "UPDATE_ADAPTIVE_RELAXED", "TaoADMMUpdateType", "TAO_ADMM_", NULL};
     25: const char *const TaoALMMTypes[]            = {"CLASSIC", "PHR", "TaoALMMType", "TAO_ALMM_", NULL};

     27: static PetscErrorCode TaoADMMToleranceUpdate(Tao tao)
     28: {
     29:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
     30:   PetscReal Axnorm, Bznorm, ATynorm, temp;
     31:   Vec       tempJR, tempL;
     32:   Tao       mis;

     34:   PetscFunctionBegin;
     35:   mis    = am->subsolverX;
     36:   tempJR = am->workJacobianRight;
     37:   tempL  = am->workLeft;
     38:   /* ATy */
     39:   PetscCall(TaoComputeJacobianEquality(mis, am->y, mis->jacobian_equality, mis->jacobian_equality_pre));
     40:   PetscCall(MatMultTranspose(mis->jacobian_equality, am->y, tempJR));
     41:   PetscCall(VecNorm(tempJR, NORM_2, &ATynorm));
     42:   /* dualres = mu * ||AT(Bz-Bzold)||_2 */
     43:   PetscCall(VecWAXPY(tempJR, -1., am->Bzold, am->Bz));
     44:   PetscCall(MatMultTranspose(mis->jacobian_equality, tempJR, tempL));
     45:   PetscCall(VecNorm(tempL, NORM_2, &am->dualres));
     46:   am->dualres *= am->mu;

     48:   /* ||Ax||_2, ||Bz||_2 */
     49:   PetscCall(VecNorm(am->Ax, NORM_2, &Axnorm));
     50:   PetscCall(VecNorm(am->Bz, NORM_2, &Bznorm));

     52:   /* Set catol to be catol_admm *  max{||Ax||,||Bz||,||c||} *
     53:    * Set gatol to be gatol_admm *  ||A^Ty|| *
     54:    * while cnorm is ||r_k||_2, and gnorm is ||d_k||_2 */
     55:   temp = am->catol_admm * PetscMax(Axnorm, (!am->const_norm) ? Bznorm : PetscMax(Bznorm, am->const_norm));
     56:   PetscCall(TaoSetConstraintTolerances(tao, temp, PETSC_CURRENT));
     57:   PetscCall(TaoSetTolerances(tao, am->gatol_admm * ATynorm, PETSC_CURRENT, PETSC_CURRENT));
     58:   PetscFunctionReturn(PETSC_SUCCESS);
     59: }

     61: /* Penalty Update for Adaptive ADMM. */
     62: static PetscErrorCode AdaptiveADMMPenaltyUpdate(Tao tao)
     63: {
     64:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
     65:   PetscReal ydiff_norm, yhatdiff_norm, Axdiff_norm, Bzdiff_norm, Axyhat, Bzy, a_sd, a_mg, a_k, b_sd, b_mg, b_k;
     66:   PetscBool hflag, gflag;
     67:   Vec       tempJR, tempJR2;

     69:   PetscFunctionBegin;
     70:   tempJR  = am->workJacobianRight;
     71:   tempJR2 = am->workJacobianRight2;
     72:   hflag   = PETSC_FALSE;
     73:   gflag   = PETSC_FALSE;
     74:   a_k     = -1;
     75:   b_k     = -1;

     77:   PetscCall(VecWAXPY(tempJR, -1., am->Axold, am->Ax));
     78:   PetscCall(VecWAXPY(tempJR2, -1., am->yhatold, am->yhat));
     79:   PetscCall(VecNorm(tempJR, NORM_2, &Axdiff_norm));
     80:   PetscCall(VecNorm(tempJR2, NORM_2, &yhatdiff_norm));
     81:   PetscCall(VecDot(tempJR, tempJR2, &Axyhat));

     83:   PetscCall(VecWAXPY(tempJR, -1., am->Bz0, am->Bz));
     84:   PetscCall(VecWAXPY(tempJR2, -1., am->y, am->y0));
     85:   PetscCall(VecNorm(tempJR, NORM_2, &Bzdiff_norm));
     86:   PetscCall(VecNorm(tempJR2, NORM_2, &ydiff_norm));
     87:   PetscCall(VecDot(tempJR, tempJR2, &Bzy));

     89:   if (Axyhat > am->orthval * Axdiff_norm * yhatdiff_norm + am->mueps) {
     90:     hflag = PETSC_TRUE;
     91:     a_sd  = PetscSqr(yhatdiff_norm) / Axyhat; /* alphaSD */
     92:     a_mg  = Axyhat / PetscSqr(Axdiff_norm);   /* alphaMG */
     93:     a_k   = (a_mg / a_sd) > 0.5 ? a_mg : a_sd - 0.5 * a_mg;
     94:   }
     95:   if (Bzy > am->orthval * Bzdiff_norm * ydiff_norm + am->mueps) {
     96:     gflag = PETSC_TRUE;
     97:     b_sd  = PetscSqr(ydiff_norm) / Bzy;  /* betaSD */
     98:     b_mg  = Bzy / PetscSqr(Bzdiff_norm); /* betaMG */
     99:     b_k   = (b_mg / b_sd) > 0.5 ? b_mg : b_sd - 0.5 * b_mg;
    100:   }
    101:   am->muold = am->mu;
    102:   if (gflag && hflag) {
    103:     am->mu = PetscSqrtReal(a_k * b_k);
    104:   } else if (hflag) {
    105:     am->mu = a_k;
    106:   } else if (gflag) {
    107:     am->mu = b_k;
    108:   }
    109:   if (am->mu > am->muold) am->mu = am->muold;
    110:   if (am->mu < am->mumin) am->mu = am->mumin;
    111:   PetscFunctionReturn(PETSC_SUCCESS);
    112: }

    114: static PetscErrorCode TaoADMMSetRegularizerType_ADMM(Tao tao, TaoADMMRegularizerType type)
    115: {
    116:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    118:   PetscFunctionBegin;
    119:   am->regswitch = type;
    120:   PetscFunctionReturn(PETSC_SUCCESS);
    121: }

    123: static PetscErrorCode TaoADMMGetRegularizerType_ADMM(Tao tao, TaoADMMRegularizerType *type)
    124: {
    125:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    127:   PetscFunctionBegin;
    128:   *type = am->regswitch;
    129:   PetscFunctionReturn(PETSC_SUCCESS);
    130: }

    132: static PetscErrorCode TaoADMMSetUpdateType_ADMM(Tao tao, TaoADMMUpdateType type)
    133: {
    134:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    136:   PetscFunctionBegin;
    137:   am->update = type;
    138:   PetscFunctionReturn(PETSC_SUCCESS);
    139: }

    141: static PetscErrorCode TaoADMMGetUpdateType_ADMM(Tao tao, TaoADMMUpdateType *type)
    142: {
    143:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    145:   PetscFunctionBegin;
    146:   *type = am->update;
    147:   PetscFunctionReturn(PETSC_SUCCESS);
    148: }

    150: /* This routine updates Jacobians with new x,z vectors,
    151:  * and then updates Ax and Bz vectors, then computes updated residual vector*/
    152: static PetscErrorCode ADMMUpdateConstraintResidualVector(Tao tao, Vec x, Vec z, Vec Ax, Vec Bz, Vec residual)
    153: {
    154:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
    155:   Tao       mis, reg;

    157:   PetscFunctionBegin;
    158:   mis = am->subsolverX;
    159:   reg = am->subsolverZ;
    160:   PetscCall(TaoComputeJacobianEquality(mis, x, mis->jacobian_equality, mis->jacobian_equality_pre));
    161:   PetscCall(MatMult(mis->jacobian_equality, x, Ax));
    162:   PetscCall(TaoComputeJacobianEquality(reg, z, reg->jacobian_equality, reg->jacobian_equality_pre));
    163:   PetscCall(MatMult(reg->jacobian_equality, z, Bz));

    165:   PetscCall(VecWAXPY(residual, 1., Bz, Ax));
    166:   if (am->constraint != NULL) PetscCall(VecAXPY(residual, -1., am->constraint));
    167:   PetscFunctionReturn(PETSC_SUCCESS);
    168: }

    170: /* Updates Augmented Lagrangians to given routines *
    171:  * For subsolverX, routine needs to be ComputeObjectiveAndGraidnet
    172:  * Separate Objective and Gradient routines are not supported.  */
    173: static PetscErrorCode SubObjGradUpdate(Tao tao, Vec x, PetscReal *f, Vec g, void *ptr)
    174: {
    175:   Tao       parent = (Tao)ptr;
    176:   TAO_ADMM *am     = (TAO_ADMM *)parent->data;
    177:   PetscReal temp, temp2;
    178:   Vec       tempJR;

    180:   PetscFunctionBegin;
    181:   tempJR = am->workJacobianRight;
    182:   PetscCall(ADMMUpdateConstraintResidualVector(parent, x, am->subsolverZ->solution, am->Ax, am->Bz, am->residual));
    183:   PetscCall((*am->ops->misfitobjgrad)(am->subsolverX, x, f, g, am->misfitobjgradP));

    185:   am->last_misfit_val = *f;
    186:   /* Objective  Add + yT(Ax+Bz-c) + mu/2*||Ax+Bz-c||_2^2 */
    187:   PetscCall(VecTDot(am->residual, am->y, &temp));
    188:   PetscCall(VecTDot(am->residual, am->residual, &temp2));
    189:   *f += temp + (am->mu / 2) * temp2;

    191:   /* Gradient. Add + mu*AT(Ax+Bz-c) + yTA*/
    192:   PetscCall(MatMultTranspose(tao->jacobian_equality, am->residual, tempJR));
    193:   PetscCall(VecAXPY(g, am->mu, tempJR));
    194:   PetscCall(MatMultTranspose(tao->jacobian_equality, am->y, tempJR));
    195:   PetscCall(VecAXPY(g, 1., tempJR));
    196:   PetscFunctionReturn(PETSC_SUCCESS);
    197: }

    199: /* Updates Augmented Lagrangians to given routines
    200:  * For subsolverZ, routine needs to be ComputeObjectiveAndGraidnet
    201:  * Separate Objective and Gradient routines are not supported.  */
    202: static PetscErrorCode RegObjGradUpdate(Tao tao, Vec z, PetscReal *f, Vec g, void *ptr)
    203: {
    204:   Tao       parent = (Tao)ptr;
    205:   TAO_ADMM *am     = (TAO_ADMM *)parent->data;
    206:   PetscReal temp, temp2;
    207:   Vec       tempJR;

    209:   PetscFunctionBegin;
    210:   tempJR = am->workJacobianRight;
    211:   PetscCall(ADMMUpdateConstraintResidualVector(parent, am->subsolverX->solution, z, am->Ax, am->Bz, am->residual));
    212:   PetscCall((*am->ops->regobjgrad)(am->subsolverZ, z, f, g, am->regobjgradP));
    213:   am->last_reg_val = *f;
    214:   /* Objective  Add  + yT(Ax+Bz-c) + mu/2*||Ax+Bz-c||_2^2 */
    215:   PetscCall(VecTDot(am->residual, am->y, &temp));
    216:   PetscCall(VecTDot(am->residual, am->residual, &temp2));
    217:   *f += temp + (am->mu / 2) * temp2;

    219:   /* Gradient. Add + mu*BT(Ax+Bz-c) + yTB*/
    220:   PetscCall(MatMultTranspose(am->subsolverZ->jacobian_equality, am->residual, tempJR));
    221:   PetscCall(VecAXPY(g, am->mu, tempJR));
    222:   PetscCall(MatMultTranspose(am->subsolverZ->jacobian_equality, am->y, tempJR));
    223:   PetscCall(VecAXPY(g, 1., tempJR));
    224:   PetscFunctionReturn(PETSC_SUCCESS);
    225: }

    227: /* Computes epsilon padded L1 norm lambda*sum(sqrt(x^2+eps^2)-eps */
    228: static PetscErrorCode ADMML1EpsilonNorm(Tao tao, Vec x, PetscReal eps, PetscReal *norm)
    229: {
    230:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
    231:   PetscInt  N;

    233:   PetscFunctionBegin;
    234:   PetscCall(VecGetSize(am->workLeft, &N));
    235:   PetscCall(VecPointwiseMult(am->workLeft, x, x));
    236:   PetscCall(VecShift(am->workLeft, am->l1epsilon * am->l1epsilon));
    237:   PetscCall(VecSqrtAbs(am->workLeft));
    238:   PetscCall(VecSum(am->workLeft, norm));
    239:   *norm += N * am->l1epsilon;
    240:   *norm *= am->lambda;
    241:   PetscFunctionReturn(PETSC_SUCCESS);
    242: }

    244: static PetscErrorCode ADMMInternalHessianUpdate(Mat H, Mat Constraint, PetscBool Identity, void *ptr)
    245: {
    246:   TAO_ADMM *am = (TAO_ADMM *)ptr;

    248:   PetscFunctionBegin;
    249:   switch (am->update) {
    250:   case TAO_ADMM_UPDATE_BASIC:
    251:     break;
    252:   case TAO_ADMM_UPDATE_ADAPTIVE:
    253:   case TAO_ADMM_UPDATE_ADAPTIVE_RELAXED:
    254:     if (H && (am->muold != am->mu)) {
    255:       if (!Identity) {
    256:         PetscCall(MatAXPY(H, am->mu - am->muold, Constraint, DIFFERENT_NONZERO_PATTERN));
    257:       } else {
    258:         PetscCall(MatShift(H, am->mu - am->muold));
    259:       }
    260:     }
    261:     break;
    262:   }
    263:   PetscFunctionReturn(PETSC_SUCCESS);
    264: }

    266: /* Updates Hessian - adds second derivative of augmented Lagrangian
    267:  * H \gets H + \rho*ATA
    268:   Here, \rho does not change in TAO_ADMM_UPDATE_BASIC - thus no-op
    269:   For ADAPTAIVE,ADAPTIVE_RELAXED,
    270:   H \gets H + (\rho-\rhoold)*ATA
    271:   Here, we assume that A is linear constraint i.e., does not change.
    272:   Thus, for both ADAPTIVE, and RELAXED, ATA matrix is pre-set (except for A=I (null case)) see TaoSetUp_ADMM */
    273: static PetscErrorCode SubHessianUpdate(Tao tao, Vec x, Mat H, Mat Hpre, void *ptr)
    274: {
    275:   Tao       parent = (Tao)ptr;
    276:   TAO_ADMM *am     = (TAO_ADMM *)parent->data;

    278:   PetscFunctionBegin;
    279:   if (am->Hxchange) {
    280:     /* Case where Hessian gets updated with respect to x vector input. */
    281:     PetscCall((*am->ops->misfithess)(am->subsolverX, x, H, Hpre, am->misfithessP));
    282:     PetscCall(ADMMInternalHessianUpdate(am->subsolverX->hessian, am->ATA, am->xJI, am));
    283:   } else if (am->Hxbool) {
    284:     /* Hessian doesn't get updated. H(x) = c */
    285:     /* Update Lagrangian only once per TAO call */
    286:     PetscCall(ADMMInternalHessianUpdate(am->subsolverX->hessian, am->ATA, am->xJI, am));
    287:     am->Hxbool = PETSC_FALSE;
    288:   }
    289:   PetscFunctionReturn(PETSC_SUCCESS);
    290: }

    292: /* Same as SubHessianUpdate, except for B matrix instead of A matrix */
    293: static PetscErrorCode RegHessianUpdate(Tao tao, Vec z, Mat H, Mat Hpre, void *ptr)
    294: {
    295:   Tao       parent = (Tao)ptr;
    296:   TAO_ADMM *am     = (TAO_ADMM *)parent->data;

    298:   PetscFunctionBegin;
    299:   if (am->Hzchange) {
    300:     /* Case where Hessian gets updated with respect to x vector input. */
    301:     PetscCall((*am->ops->reghess)(am->subsolverZ, z, H, Hpre, am->reghessP));
    302:     PetscCall(ADMMInternalHessianUpdate(am->subsolverZ->hessian, am->BTB, am->zJI, am));
    303:   } else if (am->Hzbool) {
    304:     /* Hessian doesn't get updated. H(x) = c */
    305:     /* Update Lagrangian only once per TAO call */
    306:     PetscCall(ADMMInternalHessianUpdate(am->subsolverZ->hessian, am->BTB, am->zJI, am));
    307:     am->Hzbool = PETSC_FALSE;
    308:   }
    309:   PetscFunctionReturn(PETSC_SUCCESS);
    310: }

    312: /* Shell Matrix routine for A matrix.
    313:  * This gets used when user puts NULL for
    314:  * TaoSetJacobianEqualityRoutine(tao, NULL,NULL, ...)
    315:  * Essentially sets A=I*/
    316: static PetscErrorCode JacobianIdentity(Mat mat, Vec in, Vec out)
    317: {
    318:   PetscFunctionBegin;
    319:   PetscCall(VecCopy(in, out));
    320:   PetscFunctionReturn(PETSC_SUCCESS);
    321: }

    323: /* Shell Matrix routine for B matrix.
    324:  * This gets used when user puts NULL for
    325:  * TaoADMMSetRegularizerConstraintJacobian(tao, NULL,NULL, ...)
    326:  * Sets B=-I */
    327: static PetscErrorCode JacobianIdentityB(Mat mat, Vec in, Vec out)
    328: {
    329:   PetscFunctionBegin;
    330:   PetscCall(VecCopy(in, out));
    331:   PetscCall(VecScale(out, -1.));
    332:   PetscFunctionReturn(PETSC_SUCCESS);
    333: }

    335: /* Solve f(x) + g(z) s.t. Ax + Bz = c */
    336: static PetscErrorCode TaoSolve_ADMM(Tao tao)
    337: {
    338:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
    339:   PetscInt  N;
    340:   PetscReal reg_func;
    341:   PetscBool is_reg_shell;
    342:   Vec       tempL;

    344:   PetscFunctionBegin;
    345:   if (am->regswitch != TAO_ADMM_REGULARIZER_SOFT_THRESH) {
    346:     PetscCheck(am->subsolverX->ops->computejacobianequality, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "Must call TaoADMMSetMisfitConstraintJacobian() first");
    347:     PetscCheck(am->subsolverZ->ops->computejacobianequality, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "Must call TaoADMMSetRegularizerConstraintJacobian() first");
    348:     if (am->constraint != NULL) PetscCall(VecNorm(am->constraint, NORM_2, &am->const_norm));
    349:   }
    350:   tempL = am->workLeft;
    351:   PetscCall(VecGetSize(tempL, &N));

    353:   if (am->Hx && am->ops->misfithess) PetscCall(TaoSetHessian(am->subsolverX, am->Hx, am->Hx, SubHessianUpdate, tao));

    355:   if (!am->zJI) {
    356:     /* Currently, B is assumed to be a linear system, i.e., not getting updated*/
    357:     PetscCall(MatTransposeMatMult(am->JB, am->JB, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &am->BTB));
    358:   }
    359:   if (!am->xJI) {
    360:     /* Currently, A is assumed to be a linear system, i.e., not getting updated*/
    361:     PetscCall(MatTransposeMatMult(am->subsolverX->jacobian_equality, am->subsolverX->jacobian_equality, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &am->ATA));
    362:   }

    364:   is_reg_shell = PETSC_FALSE;

    366:   PetscCall(PetscObjectTypeCompare((PetscObject)am->subsolverZ, TAOSHELL, &is_reg_shell));

    368:   if (!is_reg_shell) {
    369:     switch (am->regswitch) {
    370:     case TAO_ADMM_REGULARIZER_USER:
    371:       break;
    372:     case TAO_ADMM_REGULARIZER_SOFT_THRESH:
    373:       /* Soft Threshold. */
    374:       break;
    375:     }
    376:     if (am->ops->regobjgrad) PetscCall(TaoSetObjectiveAndGradient(am->subsolverZ, NULL, RegObjGradUpdate, tao));
    377:     if (am->Hz && am->ops->reghess) PetscCall(TaoSetHessian(am->subsolverZ, am->Hz, am->Hzpre, RegHessianUpdate, tao));
    378:   }

    380:   switch (am->update) {
    381:   case TAO_ADMM_UPDATE_BASIC:
    382:     if (am->subsolverX->hessian) {
    383:       /* In basic case, Hessian does not get updated w.r.t. to spectral penalty
    384:        * Here, when A is set, i.e., am->xJI, add mu*ATA to Hessian*/
    385:       if (!am->xJI) {
    386:         PetscCall(MatAXPY(am->subsolverX->hessian, am->mu, am->ATA, DIFFERENT_NONZERO_PATTERN));
    387:       } else {
    388:         PetscCall(MatShift(am->subsolverX->hessian, am->mu));
    389:       }
    390:     }
    391:     if (am->subsolverZ->hessian && am->regswitch == TAO_ADMM_REGULARIZER_USER) {
    392:       if (am->regswitch == TAO_ADMM_REGULARIZER_USER && !am->zJI) {
    393:         PetscCall(MatAXPY(am->subsolverZ->hessian, am->mu, am->BTB, DIFFERENT_NONZERO_PATTERN));
    394:       } else {
    395:         PetscCall(MatShift(am->subsolverZ->hessian, am->mu));
    396:       }
    397:     }
    398:     break;
    399:   case TAO_ADMM_UPDATE_ADAPTIVE:
    400:   case TAO_ADMM_UPDATE_ADAPTIVE_RELAXED:
    401:     break;
    402:   }

    404:   PetscCall(PetscCitationsRegister(citation, &cited));
    405:   tao->reason = TAO_CONTINUE_ITERATING;

    407:   while (tao->reason == TAO_CONTINUE_ITERATING) {
    408:     PetscTryTypeMethod(tao, update, tao->niter, tao->user_update);
    409:     PetscCall(VecCopy(am->Bz, am->Bzold));

    411:     /* x update */
    412:     PetscCall(TaoSolve(am->subsolverX));
    413:     PetscCall(TaoComputeJacobianEquality(am->subsolverX, am->subsolverX->solution, am->subsolverX->jacobian_equality, am->subsolverX->jacobian_equality_pre));
    414:     PetscCall(MatMult(am->subsolverX->jacobian_equality, am->subsolverX->solution, am->Ax));

    416:     am->Hxbool = PETSC_TRUE;

    418:     /* z update */
    419:     switch (am->regswitch) {
    420:     case TAO_ADMM_REGULARIZER_USER:
    421:       PetscCall(TaoSolve(am->subsolverZ));
    422:       break;
    423:     case TAO_ADMM_REGULARIZER_SOFT_THRESH:
    424:       /* L1 assumes A,B jacobians are identity nxn matrix */
    425:       PetscCall(VecWAXPY(am->workJacobianRight, 1 / am->mu, am->y, am->Ax));
    426:       PetscCall(TaoSoftThreshold(am->workJacobianRight, -am->lambda / am->mu, am->lambda / am->mu, am->subsolverZ->solution));
    427:       break;
    428:     }
    429:     am->Hzbool = PETSC_TRUE;
    430:     /* Returns Ax + Bz - c with updated Ax,Bz vectors */
    431:     PetscCall(ADMMUpdateConstraintResidualVector(tao, am->subsolverX->solution, am->subsolverZ->solution, am->Ax, am->Bz, am->residual));
    432:     /* Dual variable, y += y + mu*(Ax+Bz-c) */
    433:     PetscCall(VecWAXPY(am->y, am->mu, am->residual, am->yold));

    435:     /* stopping tolerance update */
    436:     PetscCall(TaoADMMToleranceUpdate(tao));

    438:     /* Updating Spectral Penalty */
    439:     switch (am->update) {
    440:     case TAO_ADMM_UPDATE_BASIC:
    441:       am->muold = am->mu;
    442:       break;
    443:     case TAO_ADMM_UPDATE_ADAPTIVE:
    444:     case TAO_ADMM_UPDATE_ADAPTIVE_RELAXED:
    445:       if (tao->niter == 0) {
    446:         PetscCall(VecCopy(am->y, am->y0));
    447:         PetscCall(VecWAXPY(am->residual, 1., am->Ax, am->Bzold));
    448:         if (am->constraint) PetscCall(VecAXPY(am->residual, -1., am->constraint));
    449:         PetscCall(VecWAXPY(am->yhatold, -am->mu, am->residual, am->yold));
    450:         PetscCall(VecCopy(am->Ax, am->Axold));
    451:         PetscCall(VecCopy(am->Bz, am->Bz0));
    452:         am->muold = am->mu;
    453:       } else if (tao->niter % am->T == 1) {
    454:         /* we have compute Bzold in a previous iteration, and we computed Ax above */
    455:         PetscCall(VecWAXPY(am->residual, 1., am->Ax, am->Bzold));
    456:         if (am->constraint) PetscCall(VecAXPY(am->residual, -1., am->constraint));
    457:         PetscCall(VecWAXPY(am->yhat, -am->mu, am->residual, am->yold));
    458:         PetscCall(AdaptiveADMMPenaltyUpdate(tao));
    459:         PetscCall(VecCopy(am->Ax, am->Axold));
    460:         PetscCall(VecCopy(am->Bz, am->Bz0));
    461:         PetscCall(VecCopy(am->yhat, am->yhatold));
    462:         PetscCall(VecCopy(am->y, am->y0));
    463:       } else {
    464:         am->muold = am->mu;
    465:       }
    466:       break;
    467:     default:
    468:       break;
    469:     }
    470:     tao->niter++;

    472:     /* Calculate original function values. misfit part was done in TaoADMMToleranceUpdate*/
    473:     switch (am->regswitch) {
    474:     case TAO_ADMM_REGULARIZER_USER:
    475:       if (is_reg_shell) {
    476:         PetscCall(ADMML1EpsilonNorm(tao, am->subsolverZ->solution, am->l1epsilon, &reg_func));
    477:       } else {
    478:         PetscCall((*am->ops->regobjgrad)(am->subsolverZ, am->subsolverX->solution, &reg_func, tempL, am->regobjgradP));
    479:       }
    480:       break;
    481:     case TAO_ADMM_REGULARIZER_SOFT_THRESH:
    482:       PetscCall(ADMML1EpsilonNorm(tao, am->subsolverZ->solution, am->l1epsilon, &reg_func));
    483:       break;
    484:     }
    485:     PetscCall(VecCopy(am->y, am->yold));
    486:     PetscCall(ADMMUpdateConstraintResidualVector(tao, am->subsolverX->solution, am->subsolverZ->solution, am->Ax, am->Bz, am->residual));
    487:     PetscCall(VecNorm(am->residual, NORM_2, &am->resnorm));
    488:     PetscCall(TaoLogConvergenceHistory(tao, am->last_misfit_val + reg_func, am->dualres, am->resnorm, tao->ksp_its));

    490:     PetscCall(TaoMonitor(tao, tao->niter, am->last_misfit_val + reg_func, am->dualres, am->resnorm, 1.0));
    491:     PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
    492:   }
    493:   /* Update vectors */
    494:   PetscCall(VecCopy(am->subsolverX->solution, tao->solution));
    495:   PetscCall(VecCopy(am->subsolverX->gradient, tao->gradient));
    496:   PetscCall(PetscObjectCompose((PetscObject)am->subsolverX, "TaoGetADMMParentTao_ADMM", NULL));
    497:   PetscCall(PetscObjectCompose((PetscObject)am->subsolverZ, "TaoGetADMMParentTao_ADMM", NULL));
    498:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetRegularizerType_C", NULL));
    499:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetRegularizerType_C", NULL));
    500:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetUpdateType_C", NULL));
    501:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetUpdateType_C", NULL));
    502:   PetscFunctionReturn(PETSC_SUCCESS);
    503: }

    505: static PetscErrorCode TaoSetFromOptions_ADMM(Tao tao, PetscOptionItems PetscOptionsObject)
    506: {
    507:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    509:   PetscFunctionBegin;
    510:   PetscOptionsHeadBegin(PetscOptionsObject, "ADMM problem that solves f(x) in a form of f(x) + g(z) subject to x - z = 0. Norm 1 and 2 are supported. Different subsolver routines can be selected. ");
    511:   PetscCall(PetscOptionsReal("-tao_admm_regularizer_coefficient", "regularizer constant", "", am->lambda, &am->lambda, NULL));
    512:   PetscCall(PetscOptionsReal("-tao_admm_spectral_penalty", "Constant for Augmented Lagrangian term.", "", am->mu, &am->mu, NULL));
    513:   PetscCall(PetscOptionsReal("-tao_admm_relaxation_parameter", "x relaxation parameter for Z update.", "", am->gamma, &am->gamma, NULL));
    514:   PetscCall(PetscOptionsReal("-tao_admm_tolerance_update_factor", "ADMM dynamic tolerance update factor.", "", am->tol, &am->tol, NULL));
    515:   PetscCall(PetscOptionsReal("-tao_admm_spectral_penalty_update_factor", "ADMM spectral penalty update curvature safeguard value.", "", am->orthval, &am->orthval, NULL));
    516:   PetscCall(PetscOptionsReal("-tao_admm_minimum_spectral_penalty", "Set ADMM minimum spectral penalty.", "", am->mumin, &am->mumin, NULL));
    517:   PetscCall(PetscOptionsEnum("-tao_admm_dual_update", "Lagrangian dual update policy", "TaoADMMUpdateType", TaoADMMUpdateTypes, (PetscEnum)am->update, (PetscEnum *)&am->update, NULL));
    518:   PetscCall(PetscOptionsEnum("-tao_admm_regularizer_type", "ADMM regularizer update rule", "TaoADMMRegularizerType", TaoADMMRegularizerTypes, (PetscEnum)am->regswitch, (PetscEnum *)&am->regswitch, NULL));
    519:   PetscOptionsHeadEnd();
    520:   PetscCall(TaoSetFromOptions(am->subsolverX));
    521:   if (am->regswitch != TAO_ADMM_REGULARIZER_SOFT_THRESH) PetscCall(TaoSetFromOptions(am->subsolverZ));
    522:   PetscFunctionReturn(PETSC_SUCCESS);
    523: }

    525: static PetscErrorCode TaoView_ADMM(Tao tao, PetscViewer viewer)
    526: {
    527:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    529:   PetscFunctionBegin;
    530:   PetscCall(PetscViewerASCIIPushTab(viewer));
    531:   PetscCall(TaoView(am->subsolverX, viewer));
    532:   PetscCall(TaoView(am->subsolverZ, viewer));
    533:   PetscCall(PetscViewerASCIIPopTab(viewer));
    534:   PetscFunctionReturn(PETSC_SUCCESS);
    535: }

    537: static PetscErrorCode TaoSetUp_ADMM(Tao tao)
    538: {
    539:   TAO_ADMM *am = (TAO_ADMM *)tao->data;
    540:   PetscInt  n, N, M;

    542:   PetscFunctionBegin;
    543:   PetscCall(VecGetLocalSize(tao->solution, &n));
    544:   PetscCall(VecGetSize(tao->solution, &N));
    545:   /* If Jacobian is given as NULL, it means Jacobian is identity matrix with size of solution vector */
    546:   if (!am->JB) {
    547:     am->zJI = PETSC_TRUE;
    548:     PetscCall(MatCreateShell(PetscObjectComm((PetscObject)tao), n, n, PETSC_DETERMINE, PETSC_DETERMINE, NULL, &am->JB));
    549:     PetscCall(MatShellSetOperation(am->JB, MATOP_MULT, (PetscErrorCodeFn *)JacobianIdentityB));
    550:     PetscCall(MatShellSetOperation(am->JB, MATOP_MULT_TRANSPOSE, (PetscErrorCodeFn *)JacobianIdentityB));
    551:     am->JBpre = am->JB;
    552:   }
    553:   if (!am->JA) {
    554:     am->xJI = PETSC_TRUE;
    555:     PetscCall(MatCreateShell(PetscObjectComm((PetscObject)tao), n, n, PETSC_DETERMINE, PETSC_DETERMINE, NULL, &am->JA));
    556:     PetscCall(MatShellSetOperation(am->JA, MATOP_MULT, (PetscErrorCodeFn *)JacobianIdentity));
    557:     PetscCall(MatShellSetOperation(am->JA, MATOP_MULT_TRANSPOSE, (PetscErrorCodeFn *)JacobianIdentity));
    558:     am->JApre = am->JA;
    559:   }
    560:   PetscCall(MatCreateVecs(am->JA, NULL, &am->Ax));
    561:   if (!tao->gradient) PetscCall(VecDuplicate(tao->solution, &tao->gradient));
    562:   PetscCall(TaoSetSolution(am->subsolverX, tao->solution));
    563:   if (!am->z) {
    564:     PetscCall(VecDuplicate(tao->solution, &am->z));
    565:     PetscCall(VecSet(am->z, 0.0));
    566:   }
    567:   PetscCall(TaoSetSolution(am->subsolverZ, am->z));
    568:   if (!am->workLeft) PetscCall(VecDuplicate(tao->solution, &am->workLeft));
    569:   if (!am->Axold) PetscCall(VecDuplicate(am->Ax, &am->Axold));
    570:   if (!am->workJacobianRight) PetscCall(VecDuplicate(am->Ax, &am->workJacobianRight));
    571:   if (!am->workJacobianRight2) PetscCall(VecDuplicate(am->Ax, &am->workJacobianRight2));
    572:   if (!am->Bz) PetscCall(VecDuplicate(am->Ax, &am->Bz));
    573:   if (!am->Bzold) PetscCall(VecDuplicate(am->Ax, &am->Bzold));
    574:   if (!am->Bz0) PetscCall(VecDuplicate(am->Ax, &am->Bz0));
    575:   if (!am->y) {
    576:     PetscCall(VecDuplicate(am->Ax, &am->y));
    577:     PetscCall(VecSet(am->y, 0.0));
    578:   }
    579:   if (!am->yold) {
    580:     PetscCall(VecDuplicate(am->Ax, &am->yold));
    581:     PetscCall(VecSet(am->yold, 0.0));
    582:   }
    583:   if (!am->y0) {
    584:     PetscCall(VecDuplicate(am->Ax, &am->y0));
    585:     PetscCall(VecSet(am->y0, 0.0));
    586:   }
    587:   if (!am->yhat) {
    588:     PetscCall(VecDuplicate(am->Ax, &am->yhat));
    589:     PetscCall(VecSet(am->yhat, 0.0));
    590:   }
    591:   if (!am->yhatold) {
    592:     PetscCall(VecDuplicate(am->Ax, &am->yhatold));
    593:     PetscCall(VecSet(am->yhatold, 0.0));
    594:   }
    595:   if (!am->residual) {
    596:     PetscCall(VecDuplicate(am->Ax, &am->residual));
    597:     PetscCall(VecSet(am->residual, 0.0));
    598:   }
    599:   if (!am->constraint) {
    600:     am->constraint = NULL;
    601:   } else {
    602:     PetscCall(VecGetSize(am->constraint, &M));
    603:     PetscCheck(M == N, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "Solution vector and constraint vector must be of same size!");
    604:   }

    606:   /* Save changed tao tolerance for adaptive tolerance */
    607:   if (tao->gatol != tao->default_gatol) am->gatol_admm = tao->gatol;
    608:   if (tao->catol != tao->default_catol) am->catol_admm = tao->catol;

    610:   /*Update spectral and dual elements to X subsolver */
    611:   PetscCall(TaoSetObjectiveAndGradient(am->subsolverX, NULL, SubObjGradUpdate, tao));
    612:   PetscCall(TaoSetJacobianEqualityRoutine(am->subsolverX, am->JA, am->JApre, am->ops->misfitjac, am->misfitjacobianP));
    613:   PetscCall(TaoSetJacobianEqualityRoutine(am->subsolverZ, am->JB, am->JBpre, am->ops->regjac, am->regjacobianP));
    614:   PetscFunctionReturn(PETSC_SUCCESS);
    615: }

    617: static PetscErrorCode TaoDestroy_ADMM(Tao tao)
    618: {
    619:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    621:   PetscFunctionBegin;
    622:   PetscCall(VecDestroy(&am->z));
    623:   PetscCall(VecDestroy(&am->Ax));
    624:   PetscCall(VecDestroy(&am->Axold));
    625:   PetscCall(VecDestroy(&am->Bz));
    626:   PetscCall(VecDestroy(&am->Bzold));
    627:   PetscCall(VecDestroy(&am->Bz0));
    628:   PetscCall(VecDestroy(&am->residual));
    629:   PetscCall(VecDestroy(&am->y));
    630:   PetscCall(VecDestroy(&am->yold));
    631:   PetscCall(VecDestroy(&am->y0));
    632:   PetscCall(VecDestroy(&am->yhat));
    633:   PetscCall(VecDestroy(&am->yhatold));
    634:   PetscCall(VecDestroy(&am->workLeft));
    635:   PetscCall(VecDestroy(&am->workJacobianRight));
    636:   PetscCall(VecDestroy(&am->workJacobianRight2));

    638:   PetscCall(MatDestroy(&am->JA));
    639:   PetscCall(MatDestroy(&am->JB));
    640:   if (!am->xJI) PetscCall(MatDestroy(&am->JApre));
    641:   if (!am->zJI) PetscCall(MatDestroy(&am->JBpre));
    642:   if (am->Hx) {
    643:     PetscCall(MatDestroy(&am->Hx));
    644:     PetscCall(MatDestroy(&am->Hxpre));
    645:   }
    646:   if (am->Hz) {
    647:     PetscCall(MatDestroy(&am->Hz));
    648:     PetscCall(MatDestroy(&am->Hzpre));
    649:   }
    650:   PetscCall(MatDestroy(&am->ATA));
    651:   PetscCall(MatDestroy(&am->BTB));
    652:   PetscCall(TaoDestroy(&am->subsolverX));
    653:   PetscCall(TaoDestroy(&am->subsolverZ));
    654:   am->parent = NULL;
    655:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetRegularizerType_C", NULL));
    656:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetRegularizerType_C", NULL));
    657:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetUpdateType_C", NULL));
    658:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetUpdateType_C", NULL));
    659:   PetscCall(PetscFree(tao->data));
    660:   PetscFunctionReturn(PETSC_SUCCESS);
    661: }

    663: /*MC
    664:   TAOADMM - Alternating direction method of multipliers method for solving linear problems with
    665:             constraints. in a $ \min_x f(x) + g(z)$  s.t. $Ax+Bz=c$.
    666:             This algorithm employs two sub Tao solvers, of which type can be specified
    667:             by the user. User need to provide ObjectiveAndGradient routine, and/or HessianRoutine for both subsolvers.
    668:             Hessians can be given boolean flag determining whether they change with respect to a input vector. This can be set via
    669:             `TaoADMMSet{Misfit,Regularizer}HessianChangeStatus()`.
    670:             Second subsolver does support `TAOSHELL`. It should be noted that L1-norm is used for objective value for `TAOSHELL` type.
    671:             There is option to set regularizer option, and currently soft-threshold is implemented. For spectral penalty update,
    672:             currently there are basic option and adaptive option.
    673:             Constraint is set at Ax+Bz=c, and A and B can be set with `TaoADMMSet{Misfit,Regularizer}ConstraintJacobian()`.
    674:             c can be set with `TaoADMMSetConstraintVectorRHS()`.
    675:             The user can also provide regularizer weight for second subsolver. {cite}`xu2017adaptive`

    677:   Options Database Keys:
    678: + -tao_admm_regularizer_coefficient        - regularizer constant (default 1.e-6)
    679: . -tao_admm_spectral_penalty               - Constant for Augmented Lagrangian term (default 1.)
    680: . -tao_admm_relaxation_parameter           - relaxation parameter for Z update (default 1.)
    681: . -tao_admm_tolerance_update_factor        - ADMM dynamic tolerance update factor (default 1.e-12)
    682: . -tao_admm_spectral_penalty_update_factor - ADMM spectral penalty update curvature safeguard value (default 0.2)
    683: . -tao_admm_minimum_spectral_penalty       - Set ADMM minimum spectral penalty (default 0)
    684: . -tao_admm_dual_update                    - Lagrangian dual update policy ("basic","adaptive","adaptive-relaxed") (default "basic")
    685: - -tao_admm_regularizer_type               - ADMM regularizer update rule ("user","soft-threshold") (default "soft-threshold")

    687:   Level: beginner

    689: .seealso: `TaoADMMSetMisfitHessianChangeStatus()`, `TaoADMMSetRegHessianChangeStatus()`, `TaoADMMGetSpectralPenalty()`,
    690:           `TaoADMMGetMisfitSubsolver()`, `TaoADMMGetRegularizationSubsolver()`, `TaoADMMSetConstraintVectorRHS()`,
    691:           `TaoADMMSetMinimumSpectralPenalty()`, `TaoADMMSetRegularizerCoefficient()`, `TaoADMMGetRegularizerCoefficient()`,
    692:           `TaoADMMSetRegularizerConstraintJacobian()`, `TaoADMMSetMisfitConstraintJacobian()`,
    693:           `TaoADMMSetMisfitObjectiveAndGradientRoutine()`, `TaoADMMSetMisfitHessianRoutine()`,
    694:           `TaoADMMSetRegularizerObjectiveAndGradientRoutine()`, `TaoADMMSetRegularizerHessianRoutine()`,
    695:           `TaoGetADMMParentTao()`, `TaoADMMGetDualVector()`, `TaoADMMSetRegularizerType()`,
    696:           `TaoADMMGetRegularizerType()`, `TaoADMMSetUpdateType()`, `TaoADMMGetUpdateType()`
    697: M*/

    699: PETSC_EXTERN PetscErrorCode TaoCreate_ADMM(Tao tao)
    700: {
    701:   TAO_ADMM *am;

    703:   PetscFunctionBegin;
    704:   PetscCall(PetscNew(&am));

    706:   tao->ops->destroy        = TaoDestroy_ADMM;
    707:   tao->ops->setup          = TaoSetUp_ADMM;
    708:   tao->ops->setfromoptions = TaoSetFromOptions_ADMM;
    709:   tao->ops->view           = TaoView_ADMM;
    710:   tao->ops->solve          = TaoSolve_ADMM;

    712:   PetscCall(TaoParametersInitialize(tao));

    714:   tao->data           = (void *)am;
    715:   am->l1epsilon       = 1e-6;
    716:   am->lambda          = 1e-4;
    717:   am->mu              = 1.;
    718:   am->muold           = 0.;
    719:   am->mueps           = PETSC_MACHINE_EPSILON;
    720:   am->mumin           = 0.;
    721:   am->orthval         = 0.2;
    722:   am->T               = 2;
    723:   am->parent          = tao;
    724:   am->update          = TAO_ADMM_UPDATE_BASIC;
    725:   am->regswitch       = TAO_ADMM_REGULARIZER_SOFT_THRESH;
    726:   am->tol             = PETSC_SMALL;
    727:   am->const_norm      = 0;
    728:   am->resnorm         = 0;
    729:   am->dualres         = 0;
    730:   am->ops->regobjgrad = NULL;
    731:   am->ops->reghess    = NULL;
    732:   am->gamma           = 1;
    733:   am->regobjgradP     = NULL;
    734:   am->reghessP        = NULL;
    735:   am->gatol_admm      = 1e-8;
    736:   am->catol_admm      = 0;
    737:   am->Hxchange        = PETSC_TRUE;
    738:   am->Hzchange        = PETSC_TRUE;
    739:   am->Hzbool          = PETSC_TRUE;
    740:   am->Hxbool          = PETSC_TRUE;

    742:   PetscCall(TaoCreate(PetscObjectComm((PetscObject)tao), &am->subsolverX));
    743:   PetscCall(TaoSetOptionsPrefix(am->subsolverX, "misfit_"));
    744:   PetscCall(PetscObjectIncrementTabLevel((PetscObject)am->subsolverX, (PetscObject)tao, 1));
    745:   PetscCall(TaoCreate(PetscObjectComm((PetscObject)tao), &am->subsolverZ));
    746:   PetscCall(TaoSetOptionsPrefix(am->subsolverZ, "reg_"));
    747:   PetscCall(PetscObjectIncrementTabLevel((PetscObject)am->subsolverZ, (PetscObject)tao, 1));

    749:   PetscCall(TaoSetType(am->subsolverX, TAONLS));
    750:   PetscCall(TaoSetType(am->subsolverZ, TAONLS));
    751:   PetscCall(PetscObjectCompose((PetscObject)am->subsolverX, "TaoGetADMMParentTao_ADMM", (PetscObject)tao));
    752:   PetscCall(PetscObjectCompose((PetscObject)am->subsolverZ, "TaoGetADMMParentTao_ADMM", (PetscObject)tao));
    753:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetRegularizerType_C", TaoADMMSetRegularizerType_ADMM));
    754:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetRegularizerType_C", TaoADMMGetRegularizerType_ADMM));
    755:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMSetUpdateType_C", TaoADMMSetUpdateType_ADMM));
    756:   PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoADMMGetUpdateType_C", TaoADMMGetUpdateType_ADMM));
    757:   PetscFunctionReturn(PETSC_SUCCESS);
    758: }

    760: /*@
    761:   TaoADMMSetMisfitHessianChangeStatus - Set boolean that determines  whether Hessian matrix of misfit subsolver changes with respect to input vector.

    763:   Collective

    765:   Input Parameters:
    766: + tao - the Tao solver context.
    767: - b   - the Hessian matrix change status boolean, `PETSC_FALSE`  when the Hessian matrix does not change, `PETSC_TRUE` otherwise.

    769:   Level: advanced

    771: .seealso: `TAOADMM`
    772: @*/
    773: PetscErrorCode TaoADMMSetMisfitHessianChangeStatus(Tao tao, PetscBool b)
    774: {
    775:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    777:   PetscFunctionBegin;
    778:   am->Hxchange = b;
    779:   PetscFunctionReturn(PETSC_SUCCESS);
    780: }

    782: /*@
    783:   TaoADMMSetRegHessianChangeStatus - Set boolean that determines whether Hessian matrix of regularization subsolver changes with respect to input vector.

    785:   Collective

    787:   Input Parameters:
    788: + tao - the `Tao` solver context
    789: - b   - the Hessian matrix change status boolean, `PETSC_FALSE` when the Hessian matrix does not change, `PETSC_TRUE` otherwise.

    791:   Level: advanced

    793: .seealso: `TAOADMM`
    794: @*/
    795: PetscErrorCode TaoADMMSetRegHessianChangeStatus(Tao tao, PetscBool b)
    796: {
    797:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    799:   PetscFunctionBegin;
    800:   am->Hzchange = b;
    801:   PetscFunctionReturn(PETSC_SUCCESS);
    802: }

    804: /*@
    805:   TaoADMMSetSpectralPenalty - Set the spectral penalty (mu) value

    807:   Collective

    809:   Input Parameters:
    810: + tao - the `Tao` solver context
    811: - mu  - spectral penalty

    813:   Level: advanced

    815: .seealso: `TaoADMMSetMinimumSpectralPenalty()`, `TAOADMM`
    816: @*/
    817: PetscErrorCode TaoADMMSetSpectralPenalty(Tao tao, PetscReal mu)
    818: {
    819:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    821:   PetscFunctionBegin;
    822:   am->mu = mu;
    823:   PetscFunctionReturn(PETSC_SUCCESS);
    824: }

    826: /*@
    827:   TaoADMMGetSpectralPenalty - Get the spectral penalty (mu) value

    829:   Collective

    831:   Input Parameter:
    832: . tao - the `Tao` solver context

    834:   Output Parameter:
    835: . mu - spectral penalty

    837:   Level: advanced

    839: .seealso: `TaoADMMSetMinimumSpectralPenalty()`, `TaoADMMSetSpectralPenalty()`, `TAOADMM`
    840: @*/
    841: PetscErrorCode TaoADMMGetSpectralPenalty(Tao tao, PetscReal *mu)
    842: {
    843:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    845:   PetscFunctionBegin;
    847:   PetscAssertPointer(mu, 2);
    848:   *mu = am->mu;
    849:   PetscFunctionReturn(PETSC_SUCCESS);
    850: }

    852: /*@
    853:   TaoADMMGetMisfitSubsolver - Get the pointer to the misfit subsolver inside `TAOADMM`

    855:   Collective

    857:   Input Parameter:
    858: . tao - the `Tao` solver context

    860:   Output Parameter:
    861: . misfit - the `Tao` subsolver context

    863:   Level: advanced

    865: .seealso: `TAOADMM`, `Tao`
    866: @*/
    867: PetscErrorCode TaoADMMGetMisfitSubsolver(Tao tao, Tao *misfit)
    868: {
    869:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    871:   PetscFunctionBegin;
    872:   *misfit = am->subsolverX;
    873:   PetscFunctionReturn(PETSC_SUCCESS);
    874: }

    876: /*@
    877:   TaoADMMGetRegularizationSubsolver - Get the pointer to the regularization subsolver inside `TAOADMM`

    879:   Collective

    881:   Input Parameter:
    882: . tao - the `Tao` solver context

    884:   Output Parameter:
    885: . reg - the `Tao` subsolver context

    887:   Level: advanced

    889: .seealso: `TAOADMM`, `Tao`
    890: @*/
    891: PetscErrorCode TaoADMMGetRegularizationSubsolver(Tao tao, Tao *reg)
    892: {
    893:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    895:   PetscFunctionBegin;
    896:   *reg = am->subsolverZ;
    897:   PetscFunctionReturn(PETSC_SUCCESS);
    898: }

    900: /*@
    901:   TaoADMMSetConstraintVectorRHS - Set the RHS constraint vector for `TAOADMM`

    903:   Collective

    905:   Input Parameters:
    906: + tao - the `Tao` solver context
    907: - c   - RHS vector

    909:   Level: advanced

    911: .seealso: `TAOADMM`
    912: @*/
    913: PetscErrorCode TaoADMMSetConstraintVectorRHS(Tao tao, Vec c)
    914: {
    915:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    917:   PetscFunctionBegin;
    918:   am->constraint = c;
    919:   PetscFunctionReturn(PETSC_SUCCESS);
    920: }

    922: /*@
    923:   TaoADMMSetMinimumSpectralPenalty - Set the minimum value for the spectral penalty

    925:   Collective

    927:   Input Parameters:
    928: + tao - the `Tao` solver context
    929: - mu  - minimum spectral penalty value

    931:   Level: advanced

    933: .seealso: `TaoADMMGetSpectralPenalty()`, `TAOADMM`
    934: @*/
    935: PetscErrorCode TaoADMMSetMinimumSpectralPenalty(Tao tao, PetscReal mu)
    936: {
    937:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    939:   PetscFunctionBegin;
    940:   am->mumin = mu;
    941:   PetscFunctionReturn(PETSC_SUCCESS);
    942: }

    944: /*@
    945:   TaoADMMSetRegularizerCoefficient - Set the regularization coefficient lambda for L1 norm regularization case

    947:   Collective

    949:   Input Parameters:
    950: + tao    - the `Tao` solver context
    951: - lambda - L1-norm regularizer coefficient

    953:   Level: advanced

    955: .seealso: `TaoADMMSetMisfitConstraintJacobian()`, `TaoADMMSetRegularizerConstraintJacobian()`, `TAOADMM`
    956: @*/
    957: PetscErrorCode TaoADMMSetRegularizerCoefficient(Tao tao, PetscReal lambda)
    958: {
    959:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    961:   PetscFunctionBegin;
    962:   am->lambda = lambda;
    963:   PetscFunctionReturn(PETSC_SUCCESS);
    964: }

    966: /*@
    967:   TaoADMMGetRegularizerCoefficient - Get the regularization coefficient lambda for L1 norm regularization case

    969:   Collective

    971:   Input Parameter:
    972: . tao - the `Tao` solver context

    974:   Output Parameter:
    975: . lambda - L1-norm regularizer coefficient

    977:   Level: advanced

    979: .seealso: `TaoADMMSetMisfitConstraintJacobian()`, `TaoADMMSetRegularizerConstraintJacobian()`, `TAOADMM`
    980: @*/
    981: PetscErrorCode TaoADMMGetRegularizerCoefficient(Tao tao, PetscReal *lambda)
    982: {
    983:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    985:   PetscFunctionBegin;
    986:   *lambda = am->lambda;
    987:   PetscFunctionReturn(PETSC_SUCCESS);
    988: }

    990: /*@C
    991:   TaoADMMSetMisfitConstraintJacobian - Set the constraint matrix B for the `TAOADMM` algorithm. Matrix B constrains the z variable.

    993:   Collective

    995:   Input Parameters:
    996: + tao  - the Tao solver context
    997: . J    - user-created misfit constraint Jacobian matrix
    998: . Jpre - user-created misfit Jacobian constraint matrix for constructing the preconditioner, often this is `J`
    999: . func - function pointer for the misfit constraint Jacobian update function
    1000: - ctx  - application context for the regularizer constraint Jacobian

    1002:   Calling sequence of func:
    1003: + tao  - the `Tao` context
    1004: . u    - in current input solution
    1005: . J    - the contribution to the misfit constraint Jacobian
    1006: . Jpre - the contribution to matrix from which to construct a preconditioner for the misfit constraint Jacobian
    1007: - ctx  - the optional application context

    1009:   Level: advanced

    1011: .seealso: `TaoADMMSetRegularizerCoefficient()`, `TaoADMMSetRegularizerConstraintJacobian()`, `TAOADMM`
    1012: @*/
    1013: PetscErrorCode TaoADMMSetMisfitConstraintJacobian(Tao tao, Mat J, Mat Jpre, PetscErrorCode (*func)(Tao tao, Vec u, Mat J, Mat Jpre, PetscCtx ctx), PetscCtx ctx)
    1014: {
    1015:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1017:   PetscFunctionBegin;
    1019:   if (J) {
    1021:     PetscCheckSameComm(tao, 1, J, 2);
    1022:   }
    1023:   if (Jpre) {
    1025:     PetscCheckSameComm(tao, 1, Jpre, 3);
    1026:   }
    1027:   if (ctx) am->misfitjacobianP = ctx;
    1028:   if (func) am->ops->misfitjac = func;

    1030:   if (J) {
    1031:     PetscCall(PetscObjectReference((PetscObject)J));
    1032:     PetscCall(MatDestroy(&am->JA));
    1033:     am->JA = J;
    1034:   }
    1035:   if (Jpre) {
    1036:     PetscCall(PetscObjectReference((PetscObject)Jpre));
    1037:     PetscCall(MatDestroy(&am->JApre));
    1038:     am->JApre = Jpre;
    1039:   }
    1040:   PetscFunctionReturn(PETSC_SUCCESS);
    1041: }

    1043: /*@C
    1044:   TaoADMMSetRegularizerConstraintJacobian - Set the constraint matrix B for `TAOADMM` algorithm. Matrix B constraints z variable.

    1046:   Collective

    1048:   Input Parameters:
    1049: + tao  - the `Tao` solver context
    1050: . J    - user-created regularizer constraint Jacobian matrix
    1051: . Jpre - user-created regularizer Jacobian constraint matrix for constructing the preconditioner, often this is `J`
    1052: . func - function pointer for the regularizer constraint Jacobian update function
    1053: - ctx  - application context for the regularizer constraint Jacobian

    1055:   Calling sequence of func:
    1056: + tao  - the `Tao` context
    1057: . u    - in current input solution
    1058: . J    - the contribution to the constraint Jacobian
    1059: . Jpre - the contribution to matrix from which to construct a preconditioner for the constraint Jacobian
    1060: - ctx  - the optional application context

    1062:   Level: advanced

    1064: .seealso: `TaoADMMSetRegularizerCoefficient()`, `TaoADMMSetMisfitConstraintJacobian()`, `TAOADMM`
    1065: @*/
    1066: PetscErrorCode TaoADMMSetRegularizerConstraintJacobian(Tao tao, Mat J, Mat Jpre, PetscErrorCode (*func)(Tao tao, Vec u, Mat J, Mat Jpre, PetscCtx ctx), PetscCtx ctx)
    1067: {
    1068:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1070:   PetscFunctionBegin;
    1072:   if (J) {
    1074:     PetscCheckSameComm(tao, 1, J, 2);
    1075:   }
    1076:   if (Jpre) {
    1078:     PetscCheckSameComm(tao, 1, Jpre, 3);
    1079:   }
    1080:   if (ctx) am->regjacobianP = ctx;
    1081:   if (func) am->ops->regjac = func;

    1083:   if (J) {
    1084:     PetscCall(PetscObjectReference((PetscObject)J));
    1085:     PetscCall(MatDestroy(&am->JB));
    1086:     am->JB = J;
    1087:   }
    1088:   if (Jpre) {
    1089:     PetscCall(PetscObjectReference((PetscObject)Jpre));
    1090:     PetscCall(MatDestroy(&am->JBpre));
    1091:     am->JBpre = Jpre;
    1092:   }
    1093:   PetscFunctionReturn(PETSC_SUCCESS);
    1094: }

    1096: /*@C
    1097:   TaoADMMSetMisfitObjectiveAndGradientRoutine - Sets the user-defined misfit call-back function

    1099:   Collective

    1101:   Input Parameters:
    1102: + tao  - the `Tao` context
    1103: . func - function pointer for the misfit value and gradient evaluation
    1104: - ctx  - application context for the misfit

    1106:   Calling sequence of func:
    1107: + tao - the `Tao` context
    1108: . u   - in current input solution
    1109: . f   - the contribution to the objective function
    1110: . g   - the contribution to the gradient
    1111: - ctx - the optional application context

    1113:   Level: advanced

    1115: .seealso: `TAOADMM`
    1116: @*/
    1117: PetscErrorCode TaoADMMSetMisfitObjectiveAndGradientRoutine(Tao tao, PetscErrorCode (*func)(Tao tao, Vec u, PetscReal *f, Vec g, PetscCtx ctx), PetscCtx ctx)
    1118: {
    1119:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1121:   PetscFunctionBegin;
    1123:   am->misfitobjgradP     = ctx;
    1124:   am->ops->misfitobjgrad = func;
    1125:   PetscFunctionReturn(PETSC_SUCCESS);
    1126: }

    1128: /*@C
    1129:   TaoADMMSetMisfitHessianRoutine - Sets the user-defined misfit Hessian call-back
    1130:   function into the algorithm, to be used for subsolverX.

    1132:   Collective

    1134:   Input Parameters:
    1135: + tao  - the `Tao` context
    1136: . H    - user-created matrix for the Hessian of the misfit term
    1137: . Hpre - user-created matrix for the preconditioner of Hessian of the misfit term
    1138: . func - function pointer for the misfit Hessian evaluation
    1139: - ctx  - application context for the misfit Hessian

    1141:   Calling sequence of func:
    1142: + tao  - the `Tao` context
    1143: . u    - in current input solution
    1144: . H    - output, the contribution to the Hessian matrix
    1145: . Hpre - an optional contribution to an alternative matrix with which the preconditioner is to be constructed
    1146: - ctx  - the optional application context

    1148:   Level: advanced

    1150: .seealso: `TAOADMM`
    1151: @*/
    1152: PetscErrorCode TaoADMMSetMisfitHessianRoutine(Tao tao, Mat H, Mat Hpre, PetscErrorCode (*func)(Tao tao, Vec u, Mat H, Mat Hpre, PetscCtx ctx), PetscCtx ctx)
    1153: {
    1154:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1156:   PetscFunctionBegin;
    1158:   if (H) {
    1160:     PetscCheckSameComm(tao, 1, H, 2);
    1161:   }
    1162:   if (Hpre) {
    1164:     PetscCheckSameComm(tao, 1, Hpre, 3);
    1165:   }
    1166:   if (ctx) am->misfithessP = ctx;
    1167:   if (func) am->ops->misfithess = func;
    1168:   if (H) {
    1169:     PetscCall(PetscObjectReference((PetscObject)H));
    1170:     PetscCall(MatDestroy(&am->Hx));
    1171:     am->Hx = H;
    1172:   }
    1173:   if (Hpre) {
    1174:     PetscCall(PetscObjectReference((PetscObject)Hpre));
    1175:     PetscCall(MatDestroy(&am->Hxpre));
    1176:     am->Hxpre = Hpre;
    1177:   }
    1178:   PetscFunctionReturn(PETSC_SUCCESS);
    1179: }

    1181: /*@C
    1182:   TaoADMMSetRegularizerObjectiveAndGradientRoutine - Sets the user-defined regularizer call-back function

    1184:   Collective

    1186:   Input Parameters:
    1187: + tao  - the Tao context
    1188: . func - function pointer for the regularizer value and gradient evaluation
    1189: - ctx  - application context for the regularizer

    1191:   Calling sequence of func:
    1192: + tao - the `Tao` context
    1193: . u   - in current input solution
    1194: . f   - the contribution to the objective function
    1195: . g   - the contribution to the gradient
    1196: - ctx - the optional application context

    1198:   Level: advanced

    1200: .seealso: `TAOADMM`
    1201: @*/
    1202: PetscErrorCode TaoADMMSetRegularizerObjectiveAndGradientRoutine(Tao tao, PetscErrorCode (*func)(Tao tao, Vec u, PetscReal *f, Vec g, PetscCtx ctx), PetscCtx ctx)
    1203: {
    1204:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1206:   PetscFunctionBegin;
    1208:   am->regobjgradP     = ctx;
    1209:   am->ops->regobjgrad = func;
    1210:   PetscFunctionReturn(PETSC_SUCCESS);
    1211: }

    1213: /*@C
    1214:   TaoADMMSetRegularizerHessianRoutine - Sets the user-defined regularizer Hessian call-back
    1215:   function, to be used for subsolverZ.

    1217:   Collective

    1219:   Input Parameters:
    1220: + tao  - the `Tao` context
    1221: . H    - user-created matrix for the Hessian of the regularization term
    1222: . Hpre - user-created matrix for building the preconditioner of the Hessian of the regularization term
    1223: . func - function pointer for the regularizer Hessian evaluation
    1224: - ctx  - application context for the regularizer Hessian

    1226:   Calling sequence of func:
    1227: + tao  - the `Tao` context
    1228: . u    - in current input solution
    1229: . H    - output, the contribution to the Hessian matrix
    1230: . Hpre - an optional contribution to an alternative matrix with which the preconditioner is to be constructed
    1231: - ctx  - the optional application context

    1233:   Level: advanced

    1235: .seealso: `TAOADMM`
    1236: @*/
    1237: PetscErrorCode TaoADMMSetRegularizerHessianRoutine(Tao tao, Mat H, Mat Hpre, PetscErrorCode (*func)(Tao tao, Vec u, Mat H, Mat Hpre, PetscCtx ctx), PetscCtx ctx)
    1238: {
    1239:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1241:   PetscFunctionBegin;
    1243:   if (H) {
    1245:     PetscCheckSameComm(tao, 1, H, 2);
    1246:   }
    1247:   if (Hpre) {
    1249:     PetscCheckSameComm(tao, 1, Hpre, 3);
    1250:   }
    1251:   if (ctx) am->reghessP = ctx;
    1252:   if (func) am->ops->reghess = func;
    1253:   if (H) {
    1254:     PetscCall(PetscObjectReference((PetscObject)H));
    1255:     PetscCall(MatDestroy(&am->Hz));
    1256:     am->Hz = H;
    1257:   }
    1258:   if (Hpre) {
    1259:     PetscCall(PetscObjectReference((PetscObject)Hpre));
    1260:     PetscCall(MatDestroy(&am->Hzpre));
    1261:     am->Hzpre = Hpre;
    1262:   }
    1263:   PetscFunctionReturn(PETSC_SUCCESS);
    1264: }

    1266: /*@
    1267:   TaoGetADMMParentTao - Gets pointer to parent `TAOADMM`, used by inner subsolver.

    1269:   Collective

    1271:   Input Parameter:
    1272: . tao - the `Tao` context

    1274:   Output Parameter:
    1275: . admm_tao - the parent `Tao` context

    1277:   Level: advanced

    1279: .seealso: `TAOADMM`
    1280: @*/
    1281: PetscErrorCode TaoGetADMMParentTao(Tao tao, Tao *admm_tao)
    1282: {
    1283:   PetscFunctionBegin;
    1285:   PetscCall(PetscObjectQuery((PetscObject)tao, "TaoGetADMMParentTao_ADMM", (PetscObject *)admm_tao));
    1286:   PetscFunctionReturn(PETSC_SUCCESS);
    1287: }

    1289: /*@
    1290:   TaoADMMGetDualVector - Returns the dual vector associated with the current `TAOADMM` state

    1292:   Not Collective

    1294:   Input Parameter:
    1295: . tao - the `Tao` context

    1297:   Output Parameter:
    1298: . Y - the current solution

    1300:   Level: intermediate

    1302: .seealso: `TAOADMM`
    1303: @*/
    1304: PetscErrorCode TaoADMMGetDualVector(Tao tao, Vec *Y)
    1305: {
    1306:   TAO_ADMM *am = (TAO_ADMM *)tao->data;

    1308:   PetscFunctionBegin;
    1310:   *Y = am->y;
    1311:   PetscFunctionReturn(PETSC_SUCCESS);
    1312: }

    1314: /*@
    1315:   TaoADMMSetRegularizerType - Set regularizer type for `TAOADMM` routine

    1317:   Not Collective

    1319:   Input Parameters:
    1320: + tao  - the `Tao` context
    1321: - type - regularizer type

    1323:   Options Database Key:
    1324: . -tao_admm_regularizer_type (regularizer_user|regularizer_soft_thresh) - select the regularizer

    1326:   Level: intermediate

    1328: .seealso: `TaoADMMGetRegularizerType()`, `TaoADMMRegularizerType`, `TAOADMM`
    1329: @*/
    1330: PetscErrorCode TaoADMMSetRegularizerType(Tao tao, TaoADMMRegularizerType type)
    1331: {
    1332:   PetscFunctionBegin;
    1335:   PetscTryMethod(tao, "TaoADMMSetRegularizerType_C", (Tao, TaoADMMRegularizerType), (tao, type));
    1336:   PetscFunctionReturn(PETSC_SUCCESS);
    1337: }

    1339: /*@
    1340:   TaoADMMGetRegularizerType - Gets the type of regularizer routine for `TAOADMM`

    1342:   Not Collective

    1344:   Input Parameter:
    1345: . tao - the `Tao` context

    1347:   Output Parameter:
    1348: . type - the type of regularizer

    1350:   Level: intermediate

    1352: .seealso: `TaoADMMSetRegularizerType()`, `TaoADMMRegularizerType`, `TAOADMM`
    1353: @*/
    1354: PetscErrorCode TaoADMMGetRegularizerType(Tao tao, TaoADMMRegularizerType *type)
    1355: {
    1356:   PetscFunctionBegin;
    1358:   PetscUseMethod(tao, "TaoADMMGetRegularizerType_C", (Tao, TaoADMMRegularizerType *), (tao, type));
    1359:   PetscFunctionReturn(PETSC_SUCCESS);
    1360: }

    1362: /*@
    1363:   TaoADMMSetUpdateType - Set update routine for `TAOADMM` routine

    1365:   Not Collective

    1367:   Input Parameters:
    1368: + tao  - the `Tao` context
    1369: - type - spectral parameter update type

    1371:   Level: intermediate

    1373: .seealso: `TaoADMMGetUpdateType()`, `TaoADMMUpdateType`, `TAOADMM`
    1374: @*/
    1375: PetscErrorCode TaoADMMSetUpdateType(Tao tao, TaoADMMUpdateType type)
    1376: {
    1377:   PetscFunctionBegin;
    1380:   PetscTryMethod(tao, "TaoADMMSetUpdateType_C", (Tao, TaoADMMUpdateType), (tao, type));
    1381:   PetscFunctionReturn(PETSC_SUCCESS);
    1382: }

    1384: /*@
    1385:   TaoADMMGetUpdateType - Gets the type of spectral penalty update routine for `TAOADMM`

    1387:   Not Collective

    1389:   Input Parameter:
    1390: . tao - the `Tao` context

    1392:   Output Parameter:
    1393: . type - the type of spectral penalty update routine

    1395:   Level: intermediate

    1397: .seealso: `TaoADMMSetUpdateType()`, `TaoADMMUpdateType`, `TAOADMM`
    1398: @*/
    1399: PetscErrorCode TaoADMMGetUpdateType(Tao tao, TaoADMMUpdateType *type)
    1400: {
    1401:   PetscFunctionBegin;
    1403:   PetscUseMethod(tao, "TaoADMMGetUpdateType_C", (Tao, TaoADMMUpdateType *), (tao, type));
    1404:   PetscFunctionReturn(PETSC_SUCCESS);
    1405: }