void TaoOptimizationSolver<T>::solve ()
{
LOG_SCOPE("solve()", "TaoOptimizationSolver");
this->init ();
this->system().solution->zero();
PetscMatrix<T> * hessian = cast_ptr<PetscMatrix<T> *>(this->system().matrix);
// PetscVector<T> * gradient = cast_ptr<PetscVector<T> *>(this->system().rhs);
PetscVector<T> * x = cast_ptr<PetscVector<T> *>(this->system().solution.get());
PetscVector<T> * ceq = cast_ptr<PetscVector<T> *>(this->system().C_eq.get());
PetscMatrix<T> * ceq_jac = cast_ptr<PetscMatrix<T> *>(this->system().C_eq_jac.get());
PetscVector<T> * cineq = cast_ptr<PetscVector<T> *>(this->system().C_ineq.get());
PetscMatrix<T> * cineq_jac = cast_ptr<PetscMatrix<T> *>(this->system().C_ineq_jac.get());
PetscVector<T> * lb = cast_ptr<PetscVector<T> *>(&this->system().get_vector("lower_bounds"));
PetscVector<T> * ub = cast_ptr<PetscVector<T> *>(&this->system().get_vector("upper_bounds"));
// Set the starting guess to zero.
x->zero();
PetscErrorCode ierr = 0;
// Workaround for bug where TaoSetFromOptions *reset*
// programmatically set tolerance and max. function evaluation
// values when "-tao_type ipm" was specified on the command line: we
// call TaoSetFromOptions twice (both before and after setting
// custom options programatically)
ierr = TaoSetFromOptions(_tao);
LIBMESH_CHKERR(ierr);
// Set convergence tolerances
// f(X) - f(X*) (estimated) <= fatol
// |f(X) - f(X*)| (estimated) / |f(X)| <= frtol
// ||g(X)|| <= gatol
// ||g(X)|| / |f(X)| <= grtol
// ||g(X)|| / ||g(X0)|| <= gttol
// Command line equivalents: -tao_fatol, -tao_frtol, -tao_gatol, -tao_grtol, -tao_gttol
ierr = TaoSetTolerances(_tao,
#if PETSC_RELEASE_LESS_THAN(3,7,0)
// Releases up to 3.X.Y had fatol and frtol, after that they were removed.
// Hopefully we'll be able to know X and Y soon. Guessing at 3.7.0.
/*fatol=*/PETSC_DEFAULT,
/*frtol=*/PETSC_DEFAULT,
#endif
/*gatol=*/PETSC_DEFAULT,
/*grtol=*/this->objective_function_relative_tolerance,
/*gttol=*/PETSC_DEFAULT);
LIBMESH_CHKERR(ierr);
// Set the max-allowed number of objective function evaluations
// Command line equivalent: -tao_max_funcs
ierr = TaoSetMaximumFunctionEvaluations(_tao, this->max_objective_function_evaluations);
LIBMESH_CHKERR(ierr);
// Set the max-allowed number of optimization iterations.
// Command line equivalent: -tao_max_it
// Not implemented for now as it seems fairly similar to
// ierr = TaoSetMaximumIterations(_tao, 4);
// LIBMESH_CHKERR(ierr);
// Set solution vec and an initial guess
ierr = TaoSetInitialVector(_tao, x->vec());
LIBMESH_CHKERR(ierr);
// We have to have an objective function
libmesh_assert( this->objective_object );
// Set routines for objective, gradient, hessian evaluation
ierr = TaoSetObjectiveRoutine(_tao, __libmesh_tao_objective, this);
LIBMESH_CHKERR(ierr);
if ( this->gradient_object )
{
ierr = TaoSetGradientRoutine(_tao, __libmesh_tao_gradient, this);
LIBMESH_CHKERR(ierr);
}
if ( this->hessian_object )
{
ierr = TaoSetHessianRoutine(_tao, hessian->mat(), hessian->mat(), __libmesh_tao_hessian, this);
LIBMESH_CHKERR(ierr);
}
if ( this->lower_and_upper_bounds_object )
{
// Need to actually compute the bounds vectors first
this->lower_and_upper_bounds_object->lower_and_upper_bounds(this->system());
ierr = TaoSetVariableBounds(_tao,
lb->vec(),
ub->vec());
LIBMESH_CHKERR(ierr);
}
if ( this->equality_constraints_object )
{
ierr = TaoSetEqualityConstraintsRoutine(_tao, ceq->vec(), __libmesh_tao_equality_constraints, this);
LIBMESH_CHKERR(ierr);
}
if ( this->equality_constraints_jacobian_object )
{
ierr = TaoSetJacobianEqualityRoutine(_tao,
ceq_jac->mat(),
ceq_jac->mat(),
__libmesh_tao_equality_constraints_jacobian,
this);
LIBMESH_CHKERR(ierr);
}
// Optionally set inequality constraints
if ( this->inequality_constraints_object )
{
ierr = TaoSetInequalityConstraintsRoutine(_tao, cineq->vec(), __libmesh_tao_inequality_constraints, this);
LIBMESH_CHKERR(ierr);
}
// Optionally set inequality constraints Jacobian
if ( this->inequality_constraints_jacobian_object )
{
ierr = TaoSetJacobianInequalityRoutine(_tao,
cineq_jac->mat(),
cineq_jac->mat(),
__libmesh_tao_inequality_constraints_jacobian,
this);
LIBMESH_CHKERR(ierr);
}
// Check for Tao command line options
ierr = TaoSetFromOptions(_tao);
LIBMESH_CHKERR(ierr);
// Perform the optimization
ierr = TaoSolve(_tao);
LIBMESH_CHKERR(ierr);
// Store the convergence/divergence reason
ierr = TaoGetConvergedReason(_tao, &_reason);
LIBMESH_CHKERR(ierr);
}
PetscErrorCode main(int argc,char **argv)
{
PetscErrorCode ierr; /* used to check for functions returning nonzeros */
PetscMPIInt size;
Vec x; /* solution */
KSP ksp;
PC pc;
Vec ceq,cin;
PetscBool flg; /* A return value when checking for use options */
Tao tao; /* Tao solver context */
TaoConvergedReason reason;
AppCtx user; /* application context */
/* Initialize TAO,PETSc */
ierr = PetscInitialize(&argc,&argv,(char *)0,help);CHKERRQ(ierr);
ierr = MPI_Comm_size(PETSC_COMM_WORLD,&size);CHKERRQ(ierr);
/* Specify default parameters for the problem, check for command-line overrides */
ierr = PetscStrncpy(user.name,"HS21",8);CHKERRQ(ierr);
ierr = PetscOptionsGetString(NULL,NULL,"-cutername",user.name,24,&flg);CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD,"\n---- MAROS Problem %s -----\n",user.name);CHKERRQ(ierr);
ierr = InitializeProblem(&user);CHKERRQ(ierr);
ierr = VecDuplicate(user.d,&x);CHKERRQ(ierr);
ierr = VecDuplicate(user.beq,&ceq);CHKERRQ(ierr);
ierr = VecDuplicate(user.bin,&cin);CHKERRQ(ierr);
ierr = VecSet(x,1.0);CHKERRQ(ierr);
ierr = TaoCreate(PETSC_COMM_WORLD,&tao);CHKERRQ(ierr);
ierr = TaoSetType(tao,TAOIPM);CHKERRQ(ierr);
ierr = TaoSetInitialVector(tao,x);CHKERRQ(ierr);
ierr = TaoSetObjectiveAndGradientRoutine(tao,FormFunctionGradient,(void*)&user);CHKERRQ(ierr);
ierr = TaoSetEqualityConstraintsRoutine(tao,ceq,FormEqualityConstraints,(void*)&user);CHKERRQ(ierr);
ierr = TaoSetInequalityConstraintsRoutine(tao,cin,FormInequalityConstraints,(void*)&user);CHKERRQ(ierr);
ierr = TaoSetInequalityBounds(tao,user.bin,NULL);CHKERRQ(ierr);
ierr = TaoSetJacobianEqualityRoutine(tao,user.Aeq,user.Aeq,FormEqualityJacobian,(void*)&user);CHKERRQ(ierr);
ierr = TaoSetJacobianInequalityRoutine(tao,user.Ain,user.Ain,FormInequalityJacobian,(void*)&user);CHKERRQ(ierr);
ierr = TaoSetHessianRoutine(tao,user.H,user.H,FormHessian,(void*)&user);CHKERRQ(ierr);
ierr = TaoGetKSP(tao,&ksp);CHKERRQ(ierr);
ierr = KSPGetPC(ksp,&pc);CHKERRQ(ierr);
ierr = PCSetType(pc,PCLU);CHKERRQ(ierr);
/*
This algorithm produces matrices with zeros along the diagonal therefore we need to use
SuperLU which does partial pivoting
*/
ierr = PCFactorSetMatSolverPackage(pc,MATSOLVERSUPERLU);CHKERRQ(ierr);
ierr = KSPSetType(ksp,KSPPREONLY);CHKERRQ(ierr);
ierr = TaoSetTolerances(tao,0,0,0);CHKERRQ(ierr);
ierr = TaoSetFromOptions(tao);CHKERRQ(ierr);
ierr = TaoSolve(tao);CHKERRQ(ierr);
ierr = TaoGetConvergedReason(tao,&reason);CHKERRQ(ierr);
if (reason < 0) {
ierr = PetscPrintf(MPI_COMM_WORLD, "TAO failed to converge due to %s.\n",TaoConvergedReasons[reason]);CHKERRQ(ierr);
} else {
ierr = PetscPrintf(MPI_COMM_WORLD, "Optimization completed with status %s.\n",TaoConvergedReasons[reason]);CHKERRQ(ierr);
}
ierr = DestroyProblem(&user);CHKERRQ(ierr);
ierr = VecDestroy(&x);CHKERRQ(ierr);
ierr = VecDestroy(&ceq);CHKERRQ(ierr);
ierr = VecDestroy(&cin);CHKERRQ(ierr);
ierr = TaoDestroy(&tao);CHKERRQ(ierr);
ierr = PetscFinalize();
return ierr;
}
PetscErrorCode main(int argc,char **argv)
{
PetscErrorCode ierr; /* used to check for functions returning nonzeros */
Tao tao;
KSP ksp;
PC pc;
AppCtx user; /* application context */
ierr = PetscInitialize(&argc,&argv,(char *)0,help);
CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD,"\n---- TOY Problem -----\n");
CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD,"Solution should be f(1,1)=-2\n");
CHKERRQ(ierr);
ierr = InitializeProblem(&user);
CHKERRQ(ierr);
ierr = TaoCreate(PETSC_COMM_WORLD,&tao);
CHKERRQ(ierr);
ierr = TaoSetType(tao,TAOIPM);
CHKERRQ(ierr);
ierr = TaoSetInitialVector(tao,user.x);
CHKERRQ(ierr);
ierr = TaoSetVariableBounds(tao,user.xl,user.xu);
CHKERRQ(ierr);
ierr = TaoSetObjectiveAndGradientRoutine(tao,FormFunctionGradient,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetEqualityConstraintsRoutine(tao,user.ce,FormEqualityConstraints,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetInequalityConstraintsRoutine(tao,user.ci,FormInequalityConstraints,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetJacobianEqualityRoutine(tao,user.Ae,user.Ae,FormEqualityJacobian,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetJacobianInequalityRoutine(tao,user.Ai,user.Ai,FormInequalityJacobian,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetHessianRoutine(tao,user.H,user.H,FormHessian,(void*)&user);
CHKERRQ(ierr);
ierr = TaoSetTolerances(tao,0,0,0);
CHKERRQ(ierr);
ierr = TaoSetFromOptions(tao);
CHKERRQ(ierr);
ierr = TaoGetKSP(tao,&ksp);
CHKERRQ(ierr);
ierr = KSPGetPC(ksp,&pc);
CHKERRQ(ierr);
ierr = PCSetType(pc,PCLU);
CHKERRQ(ierr);
/*
This algorithm produces matrices with zeros along the diagonal therefore we need to use
SuperLU which does partial pivoting
*/
ierr = PCFactorSetMatSolverPackage(pc,MATSOLVERSUPERLU);
CHKERRQ(ierr);
ierr = KSPSetType(ksp,KSPPREONLY);
CHKERRQ(ierr);
ierr = KSPSetFromOptions(ksp);
CHKERRQ(ierr);
ierr = TaoSetTolerances(tao,0,0,0);
CHKERRQ(ierr);
ierr = TaoSolve(tao);
CHKERRQ(ierr);
ierr = DestroyProblem(&user);
CHKERRQ(ierr);
ierr = TaoDestroy(&tao);
CHKERRQ(ierr);
ierr = PetscFinalize();
return ierr;
}
