impls/almm/almm.c

#include <../src/tao/constrained/impls/almm/almm.h> /*I "petsctao.h" I*/
#include <petsctao.h>
#include <petsc/private/petscimpl.h>
#include <petsc/private/vecimpl.h>

static PetscErrorCode TaoALMMCombinePrimal_Private(Tao, Vec, Vec, Vec);
static PetscErrorCode TaoALMMCombineDual_Private(Tao, Vec, Vec, Vec);
static PetscErrorCode TaoALMMSplitPrimal_Private(Tao, Vec, Vec, Vec);
static PetscErrorCode TaoALMMComputeOptimalityNorms_Private(Tao);
static PetscErrorCode TaoALMMComputeAugLagAndGradient_Private(Tao);
static PetscErrorCode TaoALMMComputePHRLagAndGradient_Private(Tao);

static PetscErrorCode TaoSolve_ALMM(Tao tao)
{
  TAO_ALMM          *auglag = (TAO_ALMM *)tao->data;
  TaoConvergedReason reason;
  PetscReal          updated;

  PetscFunctionBegin;
  /* reset initial multiplier/slack guess */
  if (!tao->recycle) {
    if (tao->ineq_constrained) {
      PetscCall(VecZeroEntries(auglag->Ps));
      PetscCall(TaoALMMCombinePrimal_Private(tao, auglag->Px, auglag->Ps, auglag->P));
      PetscCall(VecSet(auglag->Yi, 1.0));
    }
    if (tao->eq_constrained) PetscCall(VecSet(auglag->Ye, 1.0));
  }

  /* compute initial nonlinear Lagrangian and its derivatives */
  PetscCall((*auglag->sub_obj)(tao));
  PetscCall(TaoALMMComputeOptimalityNorms_Private(tao));
  /* print initial step and check convergence */
  PetscCall(PetscInfo(tao, "Solving with %s formulation\n", TaoALMMTypes[auglag->type]));
  PetscCall(TaoLogConvergenceHistory(tao, auglag->Lval, auglag->gnorm, auglag->cnorm, tao->ksp_its));
  PetscCall(TaoMonitor(tao, tao->niter, auglag->fval, auglag->gnorm, auglag->cnorm, 0.0));
  PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
  /* set initial penalty factor and inner solver tolerance */
  switch (auglag->type) {
  case TAO_ALMM_CLASSIC:
    auglag->mu = auglag->mu0;
    break;
  case TAO_ALMM_PHR:
    auglag->cenorm = 0.0;
    if (tao->eq_constrained) PetscCall(VecDot(auglag->Ce, auglag->Ce, &auglag->cenorm));
    auglag->cinorm = 0.0;
    if (tao->ineq_constrained) {
      PetscCall(VecCopy(auglag->Ci, auglag->Ciwork));
      PetscCall(VecScale(auglag->Ciwork, -1.0));
      PetscCall(VecPointwiseMax(auglag->Ciwork, auglag->Cizero, auglag->Ciwork));
      PetscCall(VecDot(auglag->Ciwork, auglag->Ciwork, &auglag->cinorm));
    }
    /* determine initial penalty factor based on the balance of constraint violation and objective function value */
    auglag->mu = PetscMax(1.e-6, PetscMin(10.0, 2.0 * PetscAbsReal(auglag->fval) / (auglag->cenorm + auglag->cinorm)));
    break;
  default:
    break;
  }
  auglag->gtol = auglag->gtol0;
  PetscCall(PetscInfo(tao, "Initial penalty: %.2g\n", (double)auglag->mu));

  /* start aug-lag outer loop */
  while (tao->reason == TAO_CONTINUE_ITERATING) {
    ++tao->niter;
    /* update subsolver tolerance */
    PetscCall(PetscInfo(tao, "Subsolver tolerance: ||G|| <= %e\n", (double)auglag->gtol));
    PetscCall(TaoSetTolerances(auglag->subsolver, auglag->gtol, 0.0, 0.0));
    /* solve the bound-constrained or unconstrained subproblem */
    PetscCall(VecCopy(auglag->P, auglag->subsolver->solution));
    PetscCall(TaoSolve(auglag->subsolver));
    PetscCall(VecCopy(auglag->subsolver->solution, auglag->P));
    PetscCall(TaoGetConvergedReason(auglag->subsolver, &reason));
    tao->ksp_its += auglag->subsolver->ksp_its;
    if (reason != TAO_CONVERGED_GATOL) PetscCall(PetscInfo(tao, "Subsolver failed to converge, reason: %s\n", TaoConvergedReasons[reason]));
    /* evaluate solution and test convergence */
    PetscCall((*auglag->sub_obj)(tao));
    PetscCall(TaoALMMComputeOptimalityNorms_Private(tao));
    /* decide whether to update multipliers or not */
    updated = 0.0;
    if (auglag->cnorm <= auglag->ytol) {
      PetscCall(PetscInfo(tao, "Multipliers updated: ||C|| <= %e\n", (double)auglag->ytol));
      /* constraints are good, update multipliers and convergence tolerances */
      if (tao->eq_constrained) {
        PetscCall(VecAXPY(auglag->Ye, auglag->mu, auglag->Ce));
        PetscCall(VecSet(auglag->Cework, auglag->ye_max));
        PetscCall(VecPointwiseMin(auglag->Ye, auglag->Cework, auglag->Ye));
        PetscCall(VecSet(auglag->Cework, auglag->ye_min));
        PetscCall(VecPointwiseMax(auglag->Ye, auglag->Cework, auglag->Ye));
      }
      if (tao->ineq_constrained) {
        PetscCall(VecAXPY(auglag->Yi, auglag->mu, auglag->Ci));
        PetscCall(VecSet(auglag->Ciwork, auglag->yi_max));
        PetscCall(VecPointwiseMin(auglag->Yi, auglag->Ciwork, auglag->Yi));
        PetscCall(VecSet(auglag->Ciwork, auglag->yi_min));
        PetscCall(VecPointwiseMax(auglag->Yi, auglag->Ciwork, auglag->Yi));
      }
      /* tolerances are updated only for non-PHR methods */
      if (auglag->type != TAO_ALMM_PHR) {
        auglag->ytol = PetscMax(tao->catol, auglag->ytol / PetscPowReal(auglag->mu, auglag->mu_pow_good));
        auglag->gtol = PetscMax(tao->gatol, auglag->gtol / auglag->mu);
      }
      updated = 1.0;
    } else {
      /* constraints are bad, update penalty factor */
      auglag->mu = PetscMin(auglag->mu_max, auglag->mu_fac * auglag->mu);
      /* tolerances are reset only for non-PHR methods */
      if (auglag->type != TAO_ALMM_PHR) {
        auglag->ytol = PetscMax(tao->catol, 0.1 / PetscPowReal(auglag->mu, auglag->mu_pow_bad));
        auglag->gtol = PetscMax(tao->gatol, 1.0 / auglag->mu);
      }
      PetscCall(PetscInfo(tao, "Penalty increased: mu = %.2g\n", (double)auglag->mu));
    }
    PetscCall(TaoLogConvergenceHistory(tao, auglag->fval, auglag->gnorm, auglag->cnorm, tao->ksp_its));
    PetscCall(TaoMonitor(tao, tao->niter, auglag->fval, auglag->gnorm, auglag->cnorm, updated));
    PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoView_ALMM(Tao tao, PetscViewer viewer)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;
  PetscBool isascii;

  PetscFunctionBegin;
  PetscCall(PetscViewerASCIIPushTab(viewer));
  PetscCall(TaoView(auglag->subsolver, viewer));
  PetscCall(PetscViewerASCIIPopTab(viewer));
  PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
  if (isascii) {
    PetscCall(PetscViewerASCIIPushTab(viewer));
    PetscCall(PetscViewerASCIIPrintf(viewer, "ALMM Formulation Type: %s\n", TaoALMMTypes[auglag->type]));
    PetscCall(PetscViewerASCIIPopTab(viewer));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoSetUp_ALMM(Tao tao)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;
  VecType   vec_type;
  Vec       SL, SU;
  PetscBool is_cg = PETSC_FALSE, is_lmvm = PETSC_FALSE;

  PetscFunctionBegin;
  PetscCheck(!tao->ineq_doublesided, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "TAOALMM does not support double-sided inequality constraint definition. Please restructure your inequality constraint to fit the form c(x) >= 0.");
  PetscCheck(tao->eq_constrained || tao->ineq_constrained, PetscObjectComm((PetscObject)tao), PETSC_ERR_ORDER, "Equality and/or inequality constraints must be defined before solver setup.");
  PetscCall(TaoComputeVariableBounds(tao));
  /* alias base vectors and create extras */
  PetscCall(VecGetType(tao->solution, &vec_type));
  auglag->Px = tao->solution;
  if (!tao->gradient) { /* base gradient */
    PetscCall(VecDuplicate(tao->solution, &tao->gradient));
  }
  auglag->LgradX = tao->gradient;
  if (!auglag->Xwork) { /* opt var work vector */
    PetscCall(VecDuplicate(tao->solution, &auglag->Xwork));
  }
  if (tao->eq_constrained) {
    auglag->Ce = tao->constraints_equality;
    auglag->Ae = tao->jacobian_equality;
    if (!auglag->Ye) { /* equality multipliers */
      PetscCall(VecDuplicate(auglag->Ce, &auglag->Ye));
    }
    if (!auglag->Cework) PetscCall(VecDuplicate(auglag->Ce, &auglag->Cework));
  }
  if (tao->ineq_constrained) {
    auglag->Ci = tao->constraints_inequality;
    auglag->Ai = tao->jacobian_inequality;
    if (!auglag->Yi) { /* inequality multipliers */
      PetscCall(VecDuplicate(auglag->Ci, &auglag->Yi));
    }
    if (!auglag->Ciwork) PetscCall(VecDuplicate(auglag->Ci, &auglag->Ciwork));
    if (!auglag->Cizero) {
      PetscCall(VecDuplicate(auglag->Ci, &auglag->Cizero));
      PetscCall(VecZeroEntries(auglag->Cizero));
    }
    if (!auglag->Ps) { /* slack vars */
      PetscCall(VecDuplicate(auglag->Ci, &auglag->Ps));
    }
    if (!auglag->LgradS) { /* slack component of Lagrangian gradient */
      PetscCall(VecDuplicate(auglag->Ci, &auglag->LgradS));
    }
    /* create vector for combined primal space and the associated communication objects */
    if (!auglag->P) {
      PetscCall(PetscMalloc1(2, &auglag->Parr));
      auglag->Parr[0] = auglag->Px;
      auglag->Parr[1] = auglag->Ps;
      PetscCall(VecConcatenate(2, auglag->Parr, &auglag->P, &auglag->Pis));
      PetscCall(PetscMalloc1(2, &auglag->Pscatter));
      PetscCall(VecScatterCreate(auglag->P, auglag->Pis[0], auglag->Px, NULL, &auglag->Pscatter[0]));
      PetscCall(VecScatterCreate(auglag->P, auglag->Pis[1], auglag->Ps, NULL, &auglag->Pscatter[1]));
    }
    if (tao->eq_constrained) {
      /* create vector for combined dual space and the associated communication objects */
      if (!auglag->Y) {
        PetscCall(PetscMalloc1(2, &auglag->Yarr));
        auglag->Yarr[0] = auglag->Ye;
        auglag->Yarr[1] = auglag->Yi;
        PetscCall(VecConcatenate(2, auglag->Yarr, &auglag->Y, &auglag->Yis));
        PetscCall(PetscMalloc1(2, &auglag->Yscatter));
        PetscCall(VecScatterCreate(auglag->Y, auglag->Yis[0], auglag->Ye, NULL, &auglag->Yscatter[0]));
        PetscCall(VecScatterCreate(auglag->Y, auglag->Yis[1], auglag->Yi, NULL, &auglag->Yscatter[1]));
      }
      if (!auglag->C) PetscCall(VecDuplicate(auglag->Y, &auglag->C));
    } else {
      if (!auglag->C) auglag->C = auglag->Ci;
      if (!auglag->Y) auglag->Y = auglag->Yi;
    }
  } else {
    if (!auglag->P) auglag->P = auglag->Px;
    if (!auglag->G) auglag->G = auglag->LgradX;
    if (!auglag->C) auglag->C = auglag->Ce;
    if (!auglag->Y) auglag->Y = auglag->Ye;
  }
  /* create tao primal solution and gradient to interface with subsolver */
  PetscCall(VecDuplicate(auglag->P, &auglag->Psub));
  PetscCall(VecDuplicate(auglag->P, &auglag->G));
  /* initialize parameters */
  if (auglag->type == TAO_ALMM_PHR) {
    auglag->mu_fac = 10.0;
    auglag->yi_min = 0.0;
    auglag->ytol0  = 0.5;
    auglag->gtol0  = tao->gatol;
    if (tao->gatol_changed && tao->catol_changed) {
      PetscCall(PetscInfo(tao, "TAOALMM with PHR: different gradient and constraint tolerances are not supported, setting catol = gatol\n"));
      tao->catol = tao->gatol;
    }
  }
  /* set the Lagrangian formulation type for the subsolver */
  switch (auglag->type) {
  case TAO_ALMM_CLASSIC:
    auglag->sub_obj = TaoALMMComputeAugLagAndGradient_Private;
    break;
  case TAO_ALMM_PHR:
    auglag->sub_obj = TaoALMMComputePHRLagAndGradient_Private;
    break;
  default:
    break;
  }
  /* set up the subsolver */
  PetscCall(TaoSetSolution(auglag->subsolver, auglag->Psub));
  PetscCall(TaoSetObjective(auglag->subsolver, TaoALMMSubsolverObjective_Private, (void *)auglag));
  PetscCall(TaoSetObjectiveAndGradient(auglag->subsolver, NULL, TaoALMMSubsolverObjectiveAndGradient_Private, (void *)auglag));
  if (tao->bounded) {
    /* make sure that the subsolver is a bound-constrained method */
    PetscCall(PetscObjectTypeCompare((PetscObject)auglag->subsolver, TAOCG, &is_cg));
    PetscCall(PetscObjectTypeCompare((PetscObject)auglag->subsolver, TAOLMVM, &is_lmvm));
    if (is_cg) {
      PetscCall(TaoSetType(auglag->subsolver, TAOBNCG));
      PetscCall(PetscInfo(tao, "TAOCG detected for bound-constrained problem, switching to TAOBNCG.\n"));
    }
    if (is_lmvm) {
      PetscCall(TaoSetType(auglag->subsolver, TAOBQNLS));
      PetscCall(PetscInfo(tao, "TAOLMVM detected for bound-constrained problem, switching to TAOBQNLS.\n"));
    }
    /* create lower and upper bound clone vectors for subsolver */
    if (!auglag->PL) PetscCall(VecDuplicate(auglag->P, &auglag->PL));
    if (!auglag->PU) PetscCall(VecDuplicate(auglag->P, &auglag->PU));
    if (tao->ineq_constrained) {
      /* create lower and upper bounds for slack, set lower to 0 */
      PetscCall(VecDuplicate(auglag->Ci, &SL));
      PetscCall(VecSet(SL, 0.0));
      PetscCall(VecDuplicate(auglag->Ci, &SU));
      PetscCall(VecSet(SU, PETSC_INFINITY));
      /* combine opt var bounds with slack bounds */
      PetscCall(TaoALMMCombinePrimal_Private(tao, tao->XL, SL, auglag->PL));
      PetscCall(TaoALMMCombinePrimal_Private(tao, tao->XU, SU, auglag->PU));
      /* destroy work vectors */
      PetscCall(VecDestroy(&SL));
      PetscCall(VecDestroy(&SU));
    } else {
      /* no inequality constraints, just copy bounds into the subsolver */
      PetscCall(VecCopy(tao->XL, auglag->PL));
      PetscCall(VecCopy(tao->XU, auglag->PU));
    }
    PetscCall(TaoSetVariableBounds(auglag->subsolver, auglag->PL, auglag->PU));
  }
  PetscCall(TaoSetUp(auglag->subsolver));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoDestroy_ALMM(Tao tao)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  PetscCall(TaoDestroy(&auglag->subsolver));
  PetscCall(VecDestroy(&auglag->Psub));
  PetscCall(VecDestroy(&auglag->G));
  if (tao->setupcalled) {
    PetscCall(VecDestroy(&auglag->Xwork));
    if (tao->eq_constrained) {
      PetscCall(VecDestroy(&auglag->Ye));     /* equality multipliers */
      PetscCall(VecDestroy(&auglag->Cework)); /* equality work vector */
    }
    if (tao->ineq_constrained) {
      PetscCall(VecDestroy(&auglag->Ps));     /* slack vars */
      PetscCall(PetscFree(auglag->Parr));     /* array of primal vectors */
      PetscCall(VecDestroy(&auglag->LgradS)); /* slack grad */
      PetscCall(VecDestroy(&auglag->Cizero)); /* zero vector for pointwise max */
      PetscCall(VecDestroy(&auglag->Yi));     /* inequality multipliers */
      PetscCall(VecDestroy(&auglag->Ciwork)); /* inequality work vector */
      PetscCall(VecDestroy(&auglag->P));      /* combo primal solution */
      PetscCall(ISDestroy(&auglag->Pis[0]));  /* index set for X inside P */
      PetscCall(ISDestroy(&auglag->Pis[1]));  /* index set for S inside P */
      PetscCall(PetscFree(auglag->Pis));      /* array of P index sets */
      PetscCall(VecScatterDestroy(&auglag->Pscatter[0]));
      PetscCall(VecScatterDestroy(&auglag->Pscatter[1]));
      PetscCall(PetscFree(auglag->Pscatter));
      if (tao->eq_constrained) {
        PetscCall(VecDestroy(&auglag->Y));     /* combo multipliers */
        PetscCall(PetscFree(auglag->Yarr));    /* array of dual vectors */
        PetscCall(VecDestroy(&auglag->C));     /* combo constraints */
        PetscCall(ISDestroy(&auglag->Yis[0])); /* index set for Ye inside Y */
        PetscCall(ISDestroy(&auglag->Yis[1])); /* index set for Yi inside Y */
        PetscCall(PetscFree(auglag->Yis));
        PetscCall(VecScatterDestroy(&auglag->Yscatter[0]));
        PetscCall(VecScatterDestroy(&auglag->Yscatter[1]));
        PetscCall(PetscFree(auglag->Yscatter));
      }
    }
    if (tao->bounded) {
      PetscCall(VecDestroy(&auglag->PL)); /* lower bounds for subsolver */
      PetscCall(VecDestroy(&auglag->PU)); /* upper bounds for subsolver */
    }
  }
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetType_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetType_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetSubsolver_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetSubsolver_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetMultipliers_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetMultipliers_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetPrimalIS_C", NULL));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetDualIS_C", NULL));
  PetscCall(PetscFree(tao->data));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoSetFromOptions_ALMM(Tao tao, PetscOptionItems *PetscOptionsObject)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;
  PetscInt  i;

  PetscFunctionBegin;
  PetscOptionsHeadBegin(PetscOptionsObject, "Augmented Lagrangian multiplier method solves problems with general constraints by converting them into a sequence of unconstrained problems.");
  PetscCall(PetscOptionsReal("-tao_almm_mu_init", "initial penalty parameter", "", auglag->mu0, &auglag->mu0, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_mu_factor", "increase factor for the penalty parameter", "", auglag->mu_fac, &auglag->mu_fac, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_mu_power_good", "exponential for penalty parameter when multiplier update is accepted", "", auglag->mu_pow_good, &auglag->mu_pow_good, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_mu_power_bad", "exponential for penalty parameter when multiplier update is rejected", "", auglag->mu_pow_bad, &auglag->mu_pow_bad, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_mu_max", "maximum safeguard for penalty parameter updates", "", auglag->mu_max, &auglag->mu_max, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_ye_min", "minimum safeguard for equality multiplier updates", "", auglag->ye_min, &auglag->ye_min, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_ye_max", "maximum safeguard for equality multipliers updates", "", auglag->ye_max, &auglag->ye_max, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_yi_min", "minimum safeguard for inequality multipliers updates", "", auglag->yi_min, &auglag->yi_min, NULL));
  PetscCall(PetscOptionsReal("-tao_almm_yi_max", "maximum safeguard for inequality multipliers updates", "", auglag->yi_max, &auglag->yi_max, NULL));
  PetscCall(PetscOptionsEnum("-tao_almm_type", "augmented Lagrangian formulation type for the subproblem", "TaoALMMType", TaoALMMTypes, (PetscEnum)auglag->type, (PetscEnum *)&auglag->type, NULL));
  PetscOptionsHeadEnd();
  PetscCall(TaoSetOptionsPrefix(auglag->subsolver, ((PetscObject)tao)->prefix));
  PetscCall(TaoAppendOptionsPrefix(auglag->subsolver, "tao_almm_subsolver_"));
  PetscCall(TaoSetFromOptions(auglag->subsolver));
  for (i = 0; i < tao->numbermonitors; i++) {
    PetscCall(PetscObjectReference((PetscObject)tao->monitorcontext[i]));
    PetscCall(TaoMonitorSet(auglag->subsolver, tao->monitor[i], tao->monitorcontext[i], tao->monitordestroy[i]));
    if (tao->monitor[i] == TaoMonitorDefault || tao->monitor[i] == TaoMonitorConstraintNorm || tao->monitor[i] == TaoMonitorGlobalization || tao->monitor[i] == TaoMonitorDefaultShort) auglag->info = PETSC_TRUE;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/* -------------------------------------------------------- */

/*MC
  TAOALMM - Augmented Lagrangian multiplier method for solving nonlinear optimization problems with general constraints.

  Options Database Keys:
+ -tao_almm_mu_init <real>       - initial penalty parameter (default: 10.)
. -tao_almm_mu_factor <real>     - increase factor for the penalty parameter (default: 100.)
. -tao_almm_mu_max <real>        - maximum safeguard for penalty parameter updates (default: 1.e20)
. -tao_almm_mu_power_good <real> - exponential for penalty parameter when multiplier update is accepted (default: 0.9)
. -tao_almm_mu_power_bad <real>  - exponential for penalty parameter when multiplier update is rejected (default: 0.1)
. -tao_almm_ye_min <real>        - minimum safeguard for equality multiplier updates (default: -1.e20)
. -tao_almm_ye_max <real>        - maximum safeguard for equality multiplier updates (default: 1.e20)
. -tao_almm_yi_min <real>        - minimum safeguard for inequality multiplier updates (default: -1.e20)
. -tao_almm_yi_max <real>        - maximum safeguard for inequality multiplier updates (default: 1.e20)
- -tao_almm_type <phr,classic>   - change formulation of the augmented Lagrangian merit function for the subproblem (default: phr)

  Level: beginner

  Notes:
  This method converts a constrained problem into a sequence of unconstrained problems via the augmented
  Lagrangian merit function. Bound constraints are pushed down to the subproblem without any modifications.

  Two formulations are offered for the subproblem: canonical Hestenes-Powell augmented Lagrangian with slack
  variables for inequality constraints, and a slack-less Powell-Hestenes-Rockafellar (PHR) formulation utilizing a
  pointwise max() penalty on inequality constraints. The canonical augmented Lagrangian formulation may converge
  faster for smaller problems but is highly susceptible to poor step lengths in the subproblem due to the positivity
  constraint on slack variables. PHR avoids this issue by eliminating the slack variables entirely, and is highly
  desirable for problems with a large number of inequality constraints.

  The subproblem is solved using a nested first-order TAO solver (default: `TAOBQNLS`). The user can retrieve a
  pointer to the subsolver via `TaoALMMGetSubsolver()` or pass command line arguments to it using the
  "-tao_almm_subsolver_" prefix. Currently, `TAOALMM` does not support second-order methods for the subproblem.

.vb
  while unconverged
    solve argmin_x L(x) s.t. l <= x <= u
    if ||c|| <= y_tol
      if ||c|| <= c_tol && ||Lgrad|| <= g_tol:
        problem converged, return solution
      else
        constraints sufficiently improved
        update multipliers and tighten tolerances
      endif
    else
      constraints did not improve
      update penalty and loosen tolerances
    endif
  endwhile
.ve

.seealso: `TAOALMM`, `Tao`, `TaoALMMGetType()`, `TaoALMMSetType()`, `TaoALMMSetSubsolver()`, `TaoALMMGetSubsolver()`,
          `TaoALMMGetMultipliers()`, `TaoALMMSetMultipliers()`, `TaoALMMGetPrimalIS()`, `TaoALMMGetDualIS()`
M*/
PETSC_EXTERN PetscErrorCode TaoCreate_ALMM(Tao tao)
{
  TAO_ALMM *auglag;

  PetscFunctionBegin;
  PetscCall(PetscNew(&auglag));

  tao->ops->destroy        = TaoDestroy_ALMM;
  tao->ops->setup          = TaoSetUp_ALMM;
  tao->ops->setfromoptions = TaoSetFromOptions_ALMM;
  tao->ops->view           = TaoView_ALMM;
  tao->ops->solve          = TaoSolve_ALMM;

  tao->gatol = 1.e-5;
  tao->grtol = 0.0;
  tao->gttol = 0.0;
  tao->catol = 1.e-5;
  tao->crtol = 0.0;

  tao->data           = (void *)auglag;
  auglag->parent      = tao;
  auglag->mu0         = 10.0;
  auglag->mu          = auglag->mu0;
  auglag->mu_fac      = 10.0;
  auglag->mu_max      = PETSC_INFINITY;
  auglag->mu_pow_good = 0.9;
  auglag->mu_pow_bad  = 0.1;
  auglag->ye_min      = PETSC_NINFINITY;
  auglag->ye_max      = PETSC_INFINITY;
  auglag->yi_min      = PETSC_NINFINITY;
  auglag->yi_max      = PETSC_INFINITY;
  auglag->ytol0       = 0.1 / PetscPowReal(auglag->mu0, auglag->mu_pow_bad);
  auglag->ytol        = auglag->ytol0;
  auglag->gtol0       = 1.0 / auglag->mu0;
  auglag->gtol        = auglag->gtol0;

  auglag->sub_obj = TaoALMMComputeAugLagAndGradient_Private;
  auglag->type    = TAO_ALMM_PHR;
  auglag->info    = PETSC_FALSE;

  PetscCall(TaoCreate(PetscObjectComm((PetscObject)tao), &auglag->subsolver));
  PetscCall(TaoSetType(auglag->subsolver, TAOBQNLS));
  PetscCall(TaoSetTolerances(auglag->subsolver, auglag->gtol, 0.0, 0.0));
  PetscCall(TaoSetMaximumIterations(auglag->subsolver, 1000));
  PetscCall(TaoSetMaximumFunctionEvaluations(auglag->subsolver, 10000));
  PetscCall(TaoSetFunctionLowerBound(auglag->subsolver, PETSC_NINFINITY));
  PetscCall(PetscObjectIncrementTabLevel((PetscObject)auglag->subsolver, (PetscObject)tao, 1));

  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetType_C", TaoALMMGetType_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetType_C", TaoALMMSetType_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetSubsolver_C", TaoALMMGetSubsolver_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetSubsolver_C", TaoALMMSetSubsolver_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetMultipliers_C", TaoALMMGetMultipliers_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMSetMultipliers_C", TaoALMMSetMultipliers_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetPrimalIS_C", TaoALMMGetPrimalIS_Private));
  PetscCall(PetscObjectComposeFunction((PetscObject)tao, "TaoALMMGetDualIS_C", TaoALMMGetDualIS_Private));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoALMMCombinePrimal_Private(Tao tao, Vec X, Vec S, Vec P)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  if (tao->ineq_constrained) {
    PetscCall(VecScatterBegin(auglag->Pscatter[0], X, P, INSERT_VALUES, SCATTER_REVERSE));
    PetscCall(VecScatterEnd(auglag->Pscatter[0], X, P, INSERT_VALUES, SCATTER_REVERSE));
    PetscCall(VecScatterBegin(auglag->Pscatter[1], S, P, INSERT_VALUES, SCATTER_REVERSE));
    PetscCall(VecScatterEnd(auglag->Pscatter[1], S, P, INSERT_VALUES, SCATTER_REVERSE));
  } else {
    PetscCall(VecCopy(X, P));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoALMMCombineDual_Private(Tao tao, Vec EQ, Vec IN, Vec Y)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  if (tao->eq_constrained) {
    if (tao->ineq_constrained) {
      PetscCall(VecScatterBegin(auglag->Yscatter[0], EQ, Y, INSERT_VALUES, SCATTER_REVERSE));
      PetscCall(VecScatterEnd(auglag->Yscatter[0], EQ, Y, INSERT_VALUES, SCATTER_REVERSE));
      PetscCall(VecScatterBegin(auglag->Yscatter[1], IN, Y, INSERT_VALUES, SCATTER_REVERSE));
      PetscCall(VecScatterEnd(auglag->Yscatter[1], IN, Y, INSERT_VALUES, SCATTER_REVERSE));
    } else {
      PetscCall(VecCopy(EQ, Y));
    }
  } else {
    PetscCall(VecCopy(IN, Y));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoALMMSplitPrimal_Private(Tao tao, Vec P, Vec X, Vec S)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  if (tao->ineq_constrained) {
    PetscCall(VecScatterBegin(auglag->Pscatter[0], P, X, INSERT_VALUES, SCATTER_FORWARD));
    PetscCall(VecScatterEnd(auglag->Pscatter[0], P, X, INSERT_VALUES, SCATTER_FORWARD));
    PetscCall(VecScatterBegin(auglag->Pscatter[1], P, S, INSERT_VALUES, SCATTER_FORWARD));
    PetscCall(VecScatterEnd(auglag->Pscatter[1], P, S, INSERT_VALUES, SCATTER_FORWARD));
  } else {
    PetscCall(VecCopy(P, X));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/* this assumes that the latest constraints are stored in Ce and Ci, and also combined in C */
static PetscErrorCode TaoALMMComputeOptimalityNorms_Private(Tao tao)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  /* if bounded, project the gradient */
  if (tao->bounded) PetscCall(VecBoundGradientProjection(auglag->LgradX, auglag->Px, tao->XL, tao->XU, auglag->LgradX));
  if (auglag->type == TAO_ALMM_PHR) {
    PetscCall(VecNorm(auglag->LgradX, NORM_INFINITY, &auglag->gnorm));
    auglag->cenorm = 0.0;
    if (tao->eq_constrained) PetscCall(VecNorm(auglag->Ce, NORM_INFINITY, &auglag->cenorm));
    auglag->cinorm = 0.0;
    if (tao->ineq_constrained) {
      PetscCall(VecCopy(auglag->Yi, auglag->Ciwork));
      PetscCall(VecScale(auglag->Ciwork, -1.0 / auglag->mu));
      PetscCall(VecPointwiseMax(auglag->Ciwork, auglag->Ci, auglag->Ciwork));
      PetscCall(VecNorm(auglag->Ciwork, NORM_INFINITY, &auglag->cinorm));
    }
    auglag->cnorm_old = auglag->cnorm;
    auglag->cnorm     = PetscMax(auglag->cenorm, auglag->cinorm);
    auglag->ytol      = auglag->ytol0 * auglag->cnorm_old;
  } else {
    PetscCall(VecNorm(auglag->LgradX, NORM_2, &auglag->gnorm));
    PetscCall(VecNorm(auglag->C, NORM_2, &auglag->cnorm));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode TaoALMMEvaluateIterate_Private(Tao tao)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;

  PetscFunctionBegin;
  /* split solution into primal and slack components */
  PetscCall(TaoALMMSplitPrimal_Private(tao, auglag->P, auglag->Px, auglag->Ps));

  /* compute f, df/dx and the constraints */
  PetscCall(TaoComputeObjectiveAndGradient(tao, auglag->Px, &auglag->fval, auglag->LgradX));
  if (tao->eq_constrained) {
    PetscCall(TaoComputeEqualityConstraints(tao, auglag->Px, auglag->Ce));
    PetscCall(TaoComputeJacobianEquality(tao, auglag->Px, auglag->Ae, auglag->Ae));
  }
  if (tao->ineq_constrained) {
    PetscCall(TaoComputeInequalityConstraints(tao, auglag->Px, auglag->Ci));
    PetscCall(TaoComputeJacobianInequality(tao, auglag->Px, auglag->Ai, auglag->Ai));
    switch (auglag->type) {
    case TAO_ALMM_CLASSIC:
      /* classic formulation converts inequality to equality constraints via slack variables */
      PetscCall(VecAXPY(auglag->Ci, -1.0, auglag->Ps));
      break;
    case TAO_ALMM_PHR:
      /* PHR is based on Ci <= 0 while TAO defines Ci >= 0 so we hit it with a negative sign */
      PetscCall(VecScale(auglag->Ci, -1.0));
      PetscCall(MatScale(auglag->Ai, -1.0));
      break;
    default:
      break;
    }
  }
  /* combine constraints into one vector */
  PetscCall(TaoALMMCombineDual_Private(tao, auglag->Ce, auglag->Ci, auglag->C));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*
Lphr = f + 0.5*mu*[ (Ce + Ye/mu)^T (Ce + Ye/mu) + pmin(0, Ci + Yi/mu)^T pmin(0, Ci + Yi/mu)]

dLphr/dX = dF/dX + mu*[ (Ce + Ye/mu)^T Ae + pmin(0, Ci + Yi/mu)^T Ai]

dLphr/dS = 0
*/
static PetscErrorCode TaoALMMComputePHRLagAndGradient_Private(Tao tao)
{
  TAO_ALMM *auglag  = (TAO_ALMM *)tao->data;
  PetscReal eq_norm = 0.0, ineq_norm = 0.0;

  PetscFunctionBegin;
  PetscCall(TaoALMMEvaluateIterate_Private(tao));
  if (tao->eq_constrained) {
    /* Ce_work = mu*(Ce + Ye/mu) */
    PetscCall(VecWAXPY(auglag->Cework, 1.0 / auglag->mu, auglag->Ye, auglag->Ce));
    PetscCall(VecDot(auglag->Cework, auglag->Cework, &eq_norm)); /* contribution to scalar Lagrangian */
    PetscCall(VecScale(auglag->Cework, auglag->mu));
    /* dL/dX += mu*(Ce + Ye/mu)^T Ae */
    PetscCall(MatMultTransposeAdd(auglag->Ae, auglag->Cework, auglag->LgradX, auglag->LgradX));
  }
  if (tao->ineq_constrained) {
    /* Ci_work = mu * pmax(0, Ci + Yi/mu) where pmax() is pointwise max() */
    PetscCall(VecWAXPY(auglag->Ciwork, 1.0 / auglag->mu, auglag->Yi, auglag->Ci));
    PetscCall(VecPointwiseMax(auglag->Ciwork, auglag->Cizero, auglag->Ciwork));
    PetscCall(VecDot(auglag->Ciwork, auglag->Ciwork, &ineq_norm)); /* contribution to scalar Lagrangian */
    /* dL/dX += mu * pmax(0, Ci + Yi/mu)^T Ai */
    PetscCall(VecScale(auglag->Ciwork, auglag->mu));
    PetscCall(MatMultTransposeAdd(auglag->Ai, auglag->Ciwork, auglag->LgradX, auglag->LgradX));
    /* dL/dS = 0 because there are no slacks in PHR */
    PetscCall(VecZeroEntries(auglag->LgradS));
  }
  /* combine gradient together */
  PetscCall(TaoALMMCombinePrimal_Private(tao, auglag->LgradX, auglag->LgradS, auglag->G));
  /* compute L = f + 0.5 * mu * [(Ce + Ye/mu)^T (Ce + Ye/mu) + pmax(0, Ci + Yi/mu)^T pmax(0, Ci + Yi/mu)] */
  auglag->Lval = auglag->fval + 0.5 * auglag->mu * (eq_norm + ineq_norm);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*
Lc = F + Ye^TCe + Yi^T(Ci - S) + 0.5*mu*[Ce^TCe + (Ci - S)^T(Ci - S)]

dLc/dX = dF/dX + Ye^TAe + Yi^TAi + 0.5*mu*[Ce^TAe + (Ci - S)^TAi]

dLc/dS = -[Yi + mu*(Ci - S)]
*/
static PetscErrorCode TaoALMMComputeAugLagAndGradient_Private(Tao tao)
{
  TAO_ALMM *auglag = (TAO_ALMM *)tao->data;
  PetscReal yeTce = 0.0, yiTcims = 0.0, ceTce = 0.0, cimsTcims = 0.0;

  PetscFunctionBegin;
  PetscCall(TaoALMMEvaluateIterate_Private(tao));
  if (tao->eq_constrained) {
    /* compute scalar contributions */
    PetscCall(VecDot(auglag->Ye, auglag->Ce, &yeTce));
    PetscCall(VecDot(auglag->Ce, auglag->Ce, &ceTce));
    /* dL/dX += ye^T Ae */
    PetscCall(MatMultTransposeAdd(auglag->Ae, auglag->Ye, auglag->LgradX, auglag->LgradX));
    /* dL/dX += 0.5 * mu * ce^T Ae */
    PetscCall(MatMultTranspose(auglag->Ae, auglag->Ce, auglag->Xwork));
    PetscCall(VecAXPY(auglag->LgradX, 0.5 * auglag->mu, auglag->Xwork));
  }
  if (tao->ineq_constrained) {
    /* compute scalar contributions */
    PetscCall(VecDot(auglag->Yi, auglag->Ci, &yiTcims));
    PetscCall(VecDot(auglag->Ci, auglag->Ci, &cimsTcims));
    /* dL/dX += yi^T Ai */
    PetscCall(MatMultTransposeAdd(auglag->Ai, auglag->Yi, auglag->LgradX, auglag->LgradX));
    /* dL/dX += 0.5 * mu * (ci - s)^T Ai */
    PetscCall(MatMultTranspose(auglag->Ai, auglag->Ci, auglag->Xwork));
    PetscCall(VecAXPY(auglag->LgradX, 0.5 * auglag->mu, auglag->Xwork));
    /* dL/dS = -[yi + mu*(ci - s)] */
    PetscCall(VecWAXPY(auglag->LgradS, auglag->mu, auglag->Ci, auglag->Yi));
    PetscCall(VecScale(auglag->LgradS, -1.0));
  }
  /* combine gradient together */
  PetscCall(TaoALMMCombinePrimal_Private(tao, auglag->LgradX, auglag->LgradS, auglag->G));
  /* compute L = f + ye^T ce + yi^T (ci - s) + 0.5*mu*||ce||^2 + 0.5*mu*||ci - s||^2 */
  auglag->Lval = auglag->fval + yeTce + yiTcims + 0.5 * auglag->mu * (ceTce + cimsTcims);
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode TaoALMMSubsolverObjective_Private(Tao tao, Vec P, PetscReal *Lval, void *ctx)
{
  TAO_ALMM *auglag = (TAO_ALMM *)ctx;

  PetscFunctionBegin;
  PetscCall(VecCopy(P, auglag->P));
  PetscCall((*auglag->sub_obj)(auglag->parent));
  *Lval = auglag->Lval;
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode TaoALMMSubsolverObjectiveAndGradient_Private(Tao tao, Vec P, PetscReal *Lval, Vec G, void *ctx)
{
  TAO_ALMM *auglag = (TAO_ALMM *)ctx;

  PetscFunctionBegin;
  PetscCall(VecCopy(P, auglag->P));
  PetscCall((*auglag->sub_obj)(auglag->parent));
  PetscCall(VecCopy(auglag->G, G));
  *Lval = auglag->Lval;
  PetscFunctionReturn(PETSC_SUCCESS);
}