RTOpPack::SparseSubVector
AbstractLinAlgPack::sub_vec_view(
const SpVectorSlice& sv_in
,const Range1D& rng_in
)
{
using Teuchos::null;
const Range1D rng = RangePack::full_range(rng_in,1,sv_in.dim());
const SpVectorSlice sv = sv_in(rng);
RTOpPack::SparseSubVector sub_vec;
if(!sv.nz()) {
sub_vec.initialize(
rng.lbound()-1 // global_offset
,rng.size() // sub_dim
,0 // nz
,null // vlaues
,1 // values_stride
,null // indices
,1 // indices_stride
,0 // local_offset
,1 // is_sorted
);
}
else {
SpVectorSlice::const_iterator itr = sv.begin();
TEUCHOS_TEST_FOR_EXCEPT( !( itr != sv.end() ) );
if( sv.dim() && sv.nz() == sv.dim() && sv.is_sorted() ) {
const Teuchos::ArrayRCP<const value_type> values =
Teuchos::arcp(&itr->value(), 0, values_stride*rng.size(), false) ;
sub_vec.initialize(
rng.lbound()-1 // global_offset
,rng.size() // sub_dim
,values // values
,values_stride // values_stride
);
}
else {
const Teuchos::ArrayRCP<const value_type> values =
Teuchos::arcp(&itr->value(), 0, values_stride*sv.nz(), false) ;
const Teuchos::ArrayRCP<const index_type> indexes =
Teuchos::arcp(&itr->index(), 0, indices_stride*sv.nz(), false);
sub_vec.initialize(
rng.lbound()-1 // global_offset
,sv.dim() // sub_dim
,sv.nz() // sub_nz
,values // values
,values_stride // values_stride
,indexes // indices
,indices_stride // indices_stride
,sv.offset() // local_offset
,sv.is_sorted() // is_sorted
);
}
}
return sub_vec;
}
inline
DMatrixSlice::DMatrixSlice( DMatrixSlice& gms, const Range1D& I
, const Range1D& J)
: ptr_( gms.col_ptr(1) + (I.lbound() - 1) + (J.lbound() - 1) * gms.max_rows() )
, max_rows_(gms.max_rows())
, rows_(I.size())
, cols_(J.size())
{
gms.validate_row_subscript(I.ubound());
gms.validate_col_subscript(J.ubound());
}
bool SetDBoundsStd_AddedStep::do_step(
Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type
,poss_type assoc_step_poss
)
{
NLPAlgo &algo = rsqp_algo(_algo);
NLPAlgoState &s = algo.rsqp_state();
EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level();
EJournalOutputLevel ns_olevel = algo.algo_cntr().null_space_journal_output_level();
std::ostream &out = algo.track().journal_out();
// print step header.
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
using IterationPack::print_algorithm_step;
print_algorithm_step( algo, step_poss, type, assoc_step_poss, out );
}
const Range1D
var_dep = s.var_dep(),
var_indep = s.var_indep();
const Vector
&x_k = s.x().get_k(0),
&xl = algo.nlp().xl(),
&xu = algo.nlp().xu();
VectorMutable
&dl = dl_iq_(s).set_k(0),
&du = du_iq_(s).set_k(0);
// dl = xl - x_k
LinAlgOpPack::V_VmV( &dl, xl, x_k );
// du = xu - x_k
LinAlgOpPack::V_VmV( &du, xu, x_k );
if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_VECTORS) ) {
if(var_indep.size()) {
out << "\ndl(var_indep)_k = \n" << *dl.sub_view(var_indep);
out << "\ndu(var_indep)_k = \n" << *du.sub_view(var_indep);
}
}
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) {
if(var_dep.size()) {
out << "\ndl(var_dep)_k = \n" << *dl.sub_view(var_dep);
out << "\ndu(var_dep)_k = \n" << *du.sub_view(var_dep);
}
out << "\ndl_k = \n" << dl;
out << "\ndu_k = \n" << du;
}
return true;
}
void DefaultProductVector<Scalar>::acquireNonconstDetachedVectorViewImpl(
const Range1D& rng_in, RTOpPack::SubVectorView<Scalar>* sub_vec
)
{
const Range1D
rng = rng_in.full_range() ? Range1D(0,productSpace_->dim()-1) : rng_in;
int kth_vector_space = -1;
Ordinal kth_global_offset = 0;
productSpace_->getVecSpcPoss(rng.lbound(),&kth_vector_space,&kth_global_offset);
#ifdef TEUCHOS_DEBUG
TEST_FOR_EXCEPT( !( 0 <= kth_vector_space && kth_vector_space <= numBlocks_ ) );
#endif
if(
rng.lbound() + rng.size()
<= kth_global_offset + vecs_[kth_vector_space].getConstObj()->space()->dim()
)
{
// This involves only one sub-vector so just return it.
vecs_[kth_vector_space].getConstObj()->acquireDetachedView(
rng - kth_global_offset, sub_vec
);
sub_vec->setGlobalOffset( sub_vec->globalOffset() + kth_global_offset );
}
else {
// Just let the default implementation handle this. ToDo: In the future
// we could manually construct an explicit sub-vector that spanned
// two or more constituent vectors but this would be a lot of work.
// However, this would require the use of temporary memory but
// so what.
VectorDefaultBase<Scalar>::acquireNonconstDetachedVectorViewImpl(rng_in,sub_vec);
}
}
inline
const DMatrixSlice DMatrix::operator()(const Range1D& I, const Range1D& J) const
{
Range1D Ix = RangePack::full_range(I,1,rows()), Jx = RangePack::full_range(J,1,cols());
return DMatrixSlice( const_cast<value_type*>(col_ptr(1)) + (Ix.lbound() - 1) + (Jx.lbound() - 1) * rows()
, max_rows() * cols(), max_rows(), Ix.size(), Jx.size() );
}
void MultiVectorDefaultBase<Scalar>::commitNonconstDetachedMultiVectorViewImpl(
RTOpPack::SubMultiVectorView<Scalar>* sub_mv
)
{
#ifdef TEUCHOS_DEBUG
TEUCHOS_TEST_FOR_EXCEPTION(
sub_mv==NULL, std::logic_error,
"MultiVectorDefaultBase<Scalar>::commitNonconstDetachedMultiVectorViewImpl(...): Error!"
);
#endif
// Set back the multi-vector values column by column
const Range1D rowRng(sub_mv->globalOffset(),sub_mv->globalOffset()+sub_mv->subDim()-1);
RTOpPack::SubVectorView<Scalar> msv; // uninitialized by default
for( int k = sub_mv->colOffset(); k < sub_mv->numSubCols(); ++k ) {
RCP<VectorBase<Scalar> > col_k = this->col(k);
col_k->acquireDetachedView( rowRng, &msv );
for( int i = 0; i < rowRng.size(); ++i )
msv[i] = sub_mv->values()[ i + k*rowRng.size() ];
col_k->commitDetachedView( &msv );
}
// Zero out the view
sub_mv->uninitialize();
}
RCP<MultiVectorBase<Scalar> >
DefaultColumnwiseMultiVector<Scalar>::nonconstContigSubViewImpl(
const Range1D& col_rng_in
)
{
const Ordinal numCols = domain_->dim();
const Range1D col_rng = Teuchos::full_range(col_rng_in,0,numCols-1);
#ifdef TEUCHOS_DEBUG
TEUCHOS_TEST_FOR_EXCEPTION(
!( col_rng.ubound() < numCols ), std::logic_error
,"DefaultColumnwiseMultiVector<Scalar>::subView(col_rng):"
"Error, the input range col_rng = ["<<col_rng.lbound()
<<","<<col_rng.ubound()<<"] "
"is not in the range [0,"<<(numCols-1)<<"]!"
);
#endif
return Teuchos::rcp(
new DefaultColumnwiseMultiVector<Scalar>(
range_,
domain_->smallVecSpcFcty()->createVecSpc(col_rng.size()),
col_vecs_(col_rng.lbound(),col_rng.size())
)
);
}
void EpetraVector::acquireDetachedVectorViewImpl(
const Teuchos::Range1D& rng_in,
RTOpPack::ConstSubVectorView<double>* sub_vec) const
{
/* if range is empty, return a null view */
if (rng_in == Range1D::Invalid)
{
sub_vec->initialize(rng_in.lbound(), 0, Teuchos::ArrayRCP<double>(), 1);
return ;
}
/* */
const Range1D rng = validateRange(rng_in);
/* If any requested elements are off-processor, fall back to slow
* VDB implementation */
if (
rng.lbound() < localOffset_
|| localOffset_ + localSubDim_ - 1 < rng.ubound())
{
VectorDefaultBase<double>::acquireDetachedVectorViewImpl(rng_in,sub_vec);
return ;
}
/* All requested elements are on-processor. */
const double* localValues = &(epetraVec_->operator[](0));
Teuchos::ArrayRCP<const double> locVals(localValues, rng.lbound()-localOffset_,
rng.size(), false);
// rng.ubound()-localOffset_, false);
OrdType stride = 1;
sub_vec->initialize(rng.lbound(), rng.size(),
locVals, stride);
// localValues+(rng.lbound()-localOffset_), stride);
}
void MultiVectorDefaultBase<Scalar>::acquireDetachedMultiVectorViewImpl(
const Range1D &rowRng_in,
const Range1D &colRng_in,
RTOpPack::ConstSubMultiVectorView<Scalar> *sub_mv
) const
{
const Ordinal
rangeDim = this->range()->dim(),
domainDim = this->domain()->dim();
const Range1D
rowRng = rowRng_in.full_range() ? Range1D(0,rangeDim-1) : rowRng_in,
colRng = colRng_in.full_range() ? Range1D(0,domainDim-1) : colRng_in;
#ifdef TEUCHOS_DEBUG
TEUCHOS_TEST_FOR_EXCEPTION(
!(rowRng.ubound() < rangeDim), std::out_of_range
,"MultiVectorDefaultBase<Scalar>::acquireDetachedMultiVectorViewImpl(...): Error, rowRng = ["
<<rowRng.lbound()<<","<<rowRng.ubound()<<"] is not in the range = [0,"
<<(rangeDim-1)<<"]!"
);
TEUCHOS_TEST_FOR_EXCEPTION(
!(colRng.ubound() < domainDim), std::out_of_range
,"MultiVectorDefaultBase<Scalar>::acquireDetachedMultiVectorViewImpl(...): Error, colRng = ["
<<colRng.lbound()<<","<<colRng.ubound()<<"] is not in the range = [0,"
<<(domainDim-1)<<"]!"
);
#endif
// Allocate storage for the multi-vector (stored column-major)
const ArrayRCP<Scalar> values = Teuchos::arcp<Scalar>(rowRng.size() * colRng.size());
// Extract multi-vector values column by column
RTOpPack::ConstSubVectorView<Scalar> sv; // uninitialized by default
for( int k = colRng.lbound(); k <= colRng.ubound(); ++k ) {
RCP<const VectorBase<Scalar> > col_k = this->col(k);
col_k->acquireDetachedView( rowRng, &sv );
for( int i = 0; i < rowRng.size(); ++i )
values[ i + k*rowRng.size() ] = sv[i];
col_k->releaseDetachedView( &sv );
}
// Initialize the multi-vector view object
sub_mv->initialize(
rowRng.lbound(), // globalOffset
rowRng.size(), // subDim
colRng.lbound(), // colOffset
colRng.size(), // numSubCols
values, // values
rowRng.size() // leadingDim
);
}
bool TangentialStepWithInequStd_Step::do_step(
Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type
,poss_type assoc_step_poss
)
{
using Teuchos::RCP;
using Teuchos::dyn_cast;
using ::fabs;
using LinAlgOpPack::Vt_S;
using LinAlgOpPack::V_VpV;
using LinAlgOpPack::V_VmV;
using LinAlgOpPack::Vp_StV;
using LinAlgOpPack::Vp_V;
using LinAlgOpPack::V_StV;
using LinAlgOpPack::V_MtV;
// using ConstrainedOptPack::min_abs;
using AbstractLinAlgPack::max_near_feas_step;
typedef VectorMutable::vec_mut_ptr_t vec_mut_ptr_t;
NLPAlgo &algo = rsqp_algo(_algo);
NLPAlgoState &s = algo.rsqp_state();
EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level();
EJournalOutputLevel ns_olevel = algo.algo_cntr().null_space_journal_output_level();
std::ostream &out = algo.track().journal_out();
//const bool check_results = algo.algo_cntr().check_results();
// print step header.
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
using IterationPack::print_algorithm_step;
print_algorithm_step( algo, step_poss, type, assoc_step_poss, out );
}
// problem dimensions
const size_type
//n = algo.nlp().n(),
m = algo.nlp().m(),
r = s.equ_decomp().size();
// Get the iteration quantity container objects
IterQuantityAccess<value_type>
&alpha_iq = s.alpha(),
&zeta_iq = s.zeta(),
&eta_iq = s.eta();
IterQuantityAccess<VectorMutable>
&dl_iq = dl_iq_(s),
&du_iq = du_iq_(s),
&nu_iq = s.nu(),
*c_iq = m > 0 ? &s.c() : NULL,
*lambda_iq = m > 0 ? &s.lambda() : NULL,
&rGf_iq = s.rGf(),
&w_iq = s.w(),
&qp_grad_iq = s.qp_grad(),
&py_iq = s.py(),
&pz_iq = s.pz(),
&Ypy_iq = s.Ypy(),
&Zpz_iq = s.Zpz();
IterQuantityAccess<MatrixOp>
&Z_iq = s.Z(),
//*Uz_iq = (m > r) ? &s.Uz() : NULL,
*Uy_iq = (m > r) ? &s.Uy() : NULL;
IterQuantityAccess<MatrixSymOp>
&rHL_iq = s.rHL();
IterQuantityAccess<ActSetStats>
&act_set_stats_iq = act_set_stats_(s);
// Accessed/modified/updated (just some)
VectorMutable *Ypy_k = (m ? &Ypy_iq.get_k(0) : NULL);
const MatrixOp &Z_k = Z_iq.get_k(0);
VectorMutable &pz_k = pz_iq.set_k(0);
VectorMutable &Zpz_k = Zpz_iq.set_k(0);
// Comupte qp_grad which is an approximation to rGf + Z'*HL*Y*py
// qp_grad = rGf
VectorMutable
&qp_grad_k = ( qp_grad_iq.set_k(0) = rGf_iq.get_k(0) );
// qp_grad += zeta * w
if( w_iq.updated_k(0) ) {
if(zeta_iq.updated_k(0))
Vp_StV( &qp_grad_k, zeta_iq.get_k(0), w_iq.get_k(0) );
else
Vp_V( &qp_grad_k, w_iq.get_k(0) );
}
//
// Set the bounds for:
//
// dl <= Z*pz + Y*py <= du -> dl - Ypy <= Z*pz <= du - Ypz
vec_mut_ptr_t
bl = s.space_x().create_member(),
bu = s.space_x().create_member();
if(m) {
// bl = dl_k - Ypy_k
V_VmV( bl.get(), dl_iq.get_k(0), *Ypy_k );
// bu = du_k - Ypy_k
V_VmV( bu.get(), du_iq.get_k(0), *Ypy_k );
}
else {
*bl = dl_iq.get_k(0);
*bu = du_iq.get_k(0);
}
// Print out the QP bounds for the constraints
if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_VECTORS) ) {
out << "\nqp_grad_k = \n" << qp_grad_k;
}
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) {
out << "\nbl = \n" << *bl;
out << "\nbu = \n" << *bu;
}
//
// Determine if we should perform a warm start or not.
//
bool do_warm_start = false;
if( act_set_stats_iq.updated_k(-1) ) {
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << "\nDetermining if the QP should use a warm start ...\n";
}
// We need to see if we should preform a warm start for the next iteration
ActSetStats &stats = act_set_stats_iq.get_k(-1);
const size_type
num_active = stats.num_active(),
num_adds = stats.num_adds(),
num_drops = stats.num_drops();
const value_type
frac_same
= ( num_adds == ActSetStats::NOT_KNOWN || num_active == 0
? 0.0
: my_max(((double)(num_active)-num_adds-num_drops) / num_active, 0.0 ) );
do_warm_start = ( num_active > 0 && frac_same >= warm_start_frac() );
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << "\nnum_active = " << num_active;
if( num_active ) {
out << "\nmax(num_active-num_adds-num_drops,0)/(num_active) = "
<< "max("<<num_active<<"-"<<num_adds<<"-"<<num_drops<<",0)/("<<num_active<<") = "
<< frac_same;
if( do_warm_start )
out << " >= ";
else
out << " < ";
out << "warm_start_frac = " << warm_start_frac();
}
if( do_warm_start )
out << "\nUse a warm start this time!\n";
else
out << "\nDon't use a warm start this time!\n";
}
}
// Use active set from last iteration as an estimate for current active set
// if we are to use a warm start.
//
// ToDo: If the selection of dependent and independent variables changes
// then you will have to adjust this or not perform a warm start at all!
if( do_warm_start ) {
nu_iq.set_k(0,-1);
}
else {
nu_iq.set_k(0) = 0.0; // No guess of the active set
}
VectorMutable &nu_k = nu_iq.get_k(0);
//
// Setup the reduced QP subproblem
//
// The call to the QP is setup for the more flexible call to the QPSolverRelaxed
// interface to deal with the three independent variabilities: using simple
// bounds for pz or not, general inequalities included or not, and extra equality
// constraints included or not.
// If this method of calling the QP solver were not used then 4 separate
// calls to solve_qp(...) would have to be included to handle the four possible
// QP formulations.
//
// The numeric arguments for the QP solver (in the nomenclatrue of QPSolverRelaxed)
const value_type qp_bnd_inf = NLP::infinite_bound();
const Vector &qp_g = qp_grad_k;
const MatrixSymOp &qp_G = rHL_iq.get_k(0);
const value_type qp_etaL = 0.0;
vec_mut_ptr_t qp_dL = Teuchos::null;
vec_mut_ptr_t qp_dU = Teuchos::null;
Teuchos::RCP<const MatrixOp>
qp_E = Teuchos::null;
BLAS_Cpp::Transp qp_trans_E = BLAS_Cpp::no_trans;
vec_mut_ptr_t qp_b = Teuchos::null;
vec_mut_ptr_t qp_eL = Teuchos::null;
vec_mut_ptr_t qp_eU = Teuchos::null;
Teuchos::RCP<const MatrixOp>
qp_F = Teuchos::null;
BLAS_Cpp::Transp qp_trans_F = BLAS_Cpp::no_trans;
vec_mut_ptr_t qp_f = Teuchos::null;
value_type qp_eta = 0.0;
VectorMutable &qp_d = pz_k; // pz_k will be updated directly!
vec_mut_ptr_t qp_nu = Teuchos::null;
vec_mut_ptr_t qp_mu = Teuchos::null;
vec_mut_ptr_t qp_Ed = Teuchos::null;
vec_mut_ptr_t qp_lambda = Teuchos::null;
//
// Determine if we can use simple bounds on pz.
//
// If we have a variable-reduction null-space matrix
// (with any choice for Y) then:
//
// d = Z*pz + (1-eta) * Y*py
//
// [ d(var_dep) ] = [ D ] * pz + (1-eta) * [ Ypy(var_dep) ]
// [ d(var_indep) ] [ I ] [ Ypy(var_indep) ]
//
// For a cooridinate decomposition (Y = [ I ; 0 ]) then Ypy(var_indep) ==
// 0.0 and in this case the bounds on d(var_indep) become simple bounds on
// pz even with the relaxation. Also, if dl(var_dep) and du(var_dep) are
// unbounded, then we can also use simple bounds since we don't need the
// relaxation and we can set eta=0. In this case we just have to subtract
// from the upper and lower bounds on pz!
//
// Otherwise, we can not use simple variable bounds and implement the
// relaxation properly.
//
const MatrixIdentConcat
*Zvr = dynamic_cast<const MatrixIdentConcat*>( &Z_k );
const Range1D
var_dep = Zvr ? Zvr->D_rng() : Range1D::Invalid,
var_indep = Zvr ? Zvr->I_rng() : Range1D();
RCP<Vector> Ypy_indep;
const value_type
Ypy_indep_norm_inf
= ( m ? (Ypy_indep=Ypy_k->sub_view(var_indep))->norm_inf() : 0.0);
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS )
out
<< "\nDetermine if we can use simple bounds on pz ...\n"
<< " m = " << m << std::endl
<< " dynamic_cast<const MatrixIdentConcat*>(&Z_k) = " << Zvr << std::endl
<< " ||Ypy_k(var_indep)||inf = " << Ypy_indep_norm_inf << std::endl;
const bool
bounded_var_dep
= (
m > 0
&&
num_bounded( *bl->sub_view(var_dep), *bu->sub_view(var_dep), qp_bnd_inf )
);
const bool
use_simple_pz_bounds
= (
m == 0
||
( Zvr != NULL && ( Ypy_indep_norm_inf == 0.0 || bounded_var_dep == 0 ) )
);
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS )
out
<< (use_simple_pz_bounds
? "\nUsing simple bounds on pz ...\n"
: "\nUsing bounds on full Z*pz ...\n")
<< (bounded_var_dep
? "\nThere are finite bounds on dependent variables. Adding extra inequality constrints for D*pz ...\n"
: "\nThere are no finite bounds on dependent variables. There will be no extra inequality constraints added on D*pz ...\n" ) ;
if( use_simple_pz_bounds ) {
// Set simple bound constraints on pz
qp_dL = bl->sub_view(var_indep);
qp_dU = bu->sub_view(var_indep);
qp_nu = nu_k.sub_view(var_indep); // nu_k(var_indep) will be updated directly!
if( m && bounded_var_dep ) {
// Set general inequality constraints for D*pz
qp_E = Teuchos::rcp(&Zvr->D(),false);
qp_b = Ypy_k->sub_view(var_dep);
qp_eL = bl->sub_view(var_dep);
qp_eU = bu->sub_view(var_dep);
qp_mu = nu_k.sub_view(var_dep); // nu_k(var_dep) will be updated directly!
qp_Ed = Zpz_k.sub_view(var_dep); // Zpz_k(var_dep) will be updated directly!
}
else {
// Leave these as NULL since there is no extra general inequality constraints
}
}
else if( !use_simple_pz_bounds ) { // ToDo: Leave out parts for unbounded dependent variables!
// There are no simple bounds! (leave qp_dL, qp_dU and qp_nu as null)
// Set general inequality constraints for Z*pz
qp_E = Teuchos::rcp(&Z_k,false);
qp_b = Teuchos::rcp(Ypy_k,false);
qp_eL = bl;
qp_eU = bu;
qp_mu = Teuchos::rcp(&nu_k,false);
qp_Ed = Teuchos::rcp(&Zpz_k,false); // Zpz_k will be updated directly!
}
else {
TEST_FOR_EXCEPT(true);
}
// Set the general equality constriants (if they exist)
Range1D equ_undecomp = s.equ_undecomp();
if( m > r && m > 0 ) {
// qp_f = Uy_k * py_k + c_k(equ_undecomp)
qp_f = s.space_c().sub_space(equ_undecomp)->create_member();
V_MtV( qp_f.get(), Uy_iq->get_k(0), BLAS_Cpp::no_trans, py_iq.get_k(0) );
Vp_V( qp_f.get(), *c_iq->get_k(0).sub_view(equ_undecomp) );
// Must resize for the undecomposed constriants if it has not already been
qp_F = Teuchos::rcp(&Uy_iq->get_k(0),false);
qp_lambda = lambda_iq->set_k(0).sub_view(equ_undecomp); // lambda_k(equ_undecomp), will be updated directly!
}
// Setup the rest of the arguments
QPSolverRelaxed::EOutputLevel
qp_olevel;
switch( olevel ) {
case PRINT_NOTHING:
qp_olevel = QPSolverRelaxed::PRINT_NONE;
break;
case PRINT_BASIC_ALGORITHM_INFO:
qp_olevel = QPSolverRelaxed::PRINT_NONE;
break;
case PRINT_ALGORITHM_STEPS:
qp_olevel = QPSolverRelaxed::PRINT_BASIC_INFO;
break;
case PRINT_ACTIVE_SET:
qp_olevel = QPSolverRelaxed::PRINT_ITER_SUMMARY;
break;
case PRINT_VECTORS:
qp_olevel = QPSolverRelaxed::PRINT_ITER_VECTORS;
break;
case PRINT_ITERATION_QUANTITIES:
qp_olevel = QPSolverRelaxed::PRINT_EVERY_THING;
break;
default:
TEST_FOR_EXCEPT(true);
}
// ToDo: Set print options so that only vectors matrices etc
// are only printed in the null space
//
// Solve the QP
//
qp_solver().infinite_bound(qp_bnd_inf);
const QPSolverStats::ESolutionType
solution_type =
qp_solver().solve_qp(
int(olevel) == int(PRINT_NOTHING) ? NULL : &out
,qp_olevel
,( algo.algo_cntr().check_results()
? QPSolverRelaxed::RUN_TESTS : QPSolverRelaxed::NO_TESTS )
,qp_g, qp_G, qp_etaL, qp_dL.get(), qp_dU.get()
,qp_E.get(), qp_trans_E, qp_E.get() ? qp_b.get() : NULL
,qp_E.get() ? qp_eL.get() : NULL, qp_E.get() ? qp_eU.get() : NULL
,qp_F.get(), qp_trans_F, qp_F.get() ? qp_f.get() : NULL
,NULL // obj_d
,&qp_eta, &qp_d
,qp_nu.get()
,qp_mu.get(), qp_E.get() ? qp_Ed.get() : NULL
,qp_F.get() ? qp_lambda.get() : NULL
,NULL // qp_Fd
);
//
// Check the optimality conditions for the QP
//
std::ostringstream omsg;
bool throw_qp_failure = false;
if( qp_testing() == QP_TEST
|| ( qp_testing() == QP_TEST_DEFAULT && algo.algo_cntr().check_results() ) )
{
if( int(olevel) >= int(PRINT_ALGORITHM_STEPS) ) {
out << "\nChecking the optimality conditions of the reduced QP subproblem ...\n";
}
if(!qp_tester().check_optimality_conditions(
solution_type,qp_solver().infinite_bound()
,int(olevel) == int(PRINT_NOTHING) ? NULL : &out
,int(olevel) >= int(PRINT_VECTORS) ? true : false
,int(olevel) >= int(PRINT_ITERATION_QUANTITIES) ? true : false
,qp_g, qp_G, qp_etaL, qp_dL.get(), qp_dU.get()
,qp_E.get(), qp_trans_E, qp_E.get() ? qp_b.get() : NULL
,qp_E.get() ? qp_eL.get() : NULL, qp_E.get() ? qp_eU.get() : NULL
,qp_F.get(), qp_trans_F, qp_F.get() ? qp_f.get() : NULL
,NULL // obj_d
,&qp_eta, &qp_d
,qp_nu.get()
,qp_mu.get(), qp_E.get() ? qp_Ed.get() : NULL
,qp_F.get() ? qp_lambda.get() : NULL
,NULL // qp_Fd
))
{
omsg << "\n*** Alert! at least one of the QP optimality conditions did not check out.\n";
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << omsg.str();
}
throw_qp_failure = true;
}
}
//
// Set the solution
//
if( !use_simple_pz_bounds ) {
// Everything is already updated!
}
else if( use_simple_pz_bounds ) {
// Just have to set Zpz_k(var_indep) = pz_k
*Zpz_k.sub_view(var_indep) = pz_k;
if( m && !bounded_var_dep ) {
// Must compute Zpz_k(var_dep) = D*pz
LinAlgOpPack::V_MtV( &*Zpz_k.sub_view(var_dep), Zvr->D(), BLAS_Cpp::no_trans, pz_k );
// ToDo: Remove the compuation of Zpz here unless you must
}
}
else {
TEST_FOR_EXCEPT(true);
}
// Set the solution statistics
qp_solver_stats_(s).set_k(0) = qp_solver().get_qp_stats();
// Cut back Ypy_k = (1-eta) * Ypy_k
const value_type eps = std::numeric_limits<value_type>::epsilon();
if( fabs(qp_eta - 0.0) > eps ) {
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out
<< "\n*** Alert! the QP was infeasible (eta = "<<qp_eta<<"). Cutting back Ypy_k = (1.0 - eta)*Ypy_k ...\n";
}
Vt_S( Ypy_k, 1.0 - qp_eta );
}
// eta_k
eta_iq.set_k(0) = qp_eta;
//
// Modify the solution if we have to!
//
switch(solution_type) {
case QPSolverStats::OPTIMAL_SOLUTION:
break; // we are good!
case QPSolverStats::PRIMAL_FEASIBLE_POINT:
{
omsg
<< "\n*** Alert! the returned QP solution is PRIMAL_FEASIBLE_POINT but not optimal!\n";
if( primal_feasible_point_error() )
omsg
<< "\n*** primal_feasible_point_error == true, this is an error!\n";
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << omsg.str();
}
throw_qp_failure = primal_feasible_point_error();
break;
}
case QPSolverStats::DUAL_FEASIBLE_POINT:
{
omsg
<< "\n*** Alert! the returned QP solution is DUAL_FEASIBLE_POINT"
<< "\n*** but not optimal so we cut back the step ...\n";
if( dual_feasible_point_error() )
omsg
<< "\n*** dual_feasible_point_error == true, this is an error!\n";
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << omsg.str();
}
// Cut back the step to fit in the bounds
//
// dl <= u*(Ypy_k+Zpz_k) <= du
//
vec_mut_ptr_t
zero = s.space_x().create_member(0.0),
d_tmp = s.space_x().create_member();
V_VpV( d_tmp.get(), *Ypy_k, Zpz_k );
const std::pair<value_type,value_type>
u_steps = max_near_feas_step( *zero, *d_tmp, dl_iq.get_k(0), du_iq.get_k(0), 0.0 );
const value_type
u = my_min( u_steps.first, 1.0 ); // largest positive step size
alpha_iq.set_k(0) = u;
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << "\nFinding u s.t. dl <= u*(Ypy_k+Zpz_k) <= du\n"
<< "max step length u = " << u << std::endl
<< "alpha_k = u = " << alpha_iq.get_k(0) << std::endl;
}
throw_qp_failure = dual_feasible_point_error();
break;
}
case QPSolverStats::SUBOPTIMAL_POINT:
{
omsg
<< "\n*** Alert!, the returned QP solution is SUBOPTIMAL_POINT!\n";
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << omsg.str();
}
throw_qp_failure = true;
break;
}
default:
TEST_FOR_EXCEPT(true); // should not happen!
}
//
// Output the final solution!
//
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << "\n||pz_k||inf = " << s.pz().get_k(0).norm_inf()
<< "\nnu_k.nz() = " << s.nu().get_k(0).nz()
<< "\nmax(|nu_k(i)|) = " << s.nu().get_k(0).norm_inf()
// << "\nmin(|nu_k(i)|) = " << min_abs( s.nu().get_k(0)() )
;
if( m > r ) out << "\n||lambda_k(undecomp)||inf = " << s.lambda().get_k(0).norm_inf();
out << "\n||Zpz_k||2 = " << s.Zpz().get_k(0).norm_2()
;
if(qp_eta > 0.0) out << "\n||Ypy||2 = " << s.Ypy().get_k(0).norm_2();
out << std::endl;
}
if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_VECTORS) ) {
out << "\npz_k = \n" << s.pz().get_k(0);
if(var_indep.size())
out << "\nnu_k(var_indep) = \n" << *s.nu().get_k(0).sub_view(var_indep);
}
if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_VECTORS) ) {
if(var_indep.size())
out << "\nZpz(var_indep)_k = \n" << *s.Zpz().get_k(0).sub_view(var_indep);
out << std::endl;
}
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) {
if(var_dep.size())
out << "\nZpz(var_dep)_k = \n" << *s.Zpz().get_k(0).sub_view(var_dep);
out << "\nZpz_k = \n" << s.Zpz().get_k(0);
out << std::endl;
}
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) {
out << "\nnu_k = \n" << s.nu().get_k(0);
if(var_dep.size())
out << "\nnu_k(var_dep) = \n" << *s.nu().get_k(0).sub_view(var_dep);
if( m > r )
out << "\nlambda_k(equ_undecomp) = \n" << *s.lambda().get_k(0).sub_view(equ_undecomp);
if(qp_eta > 0.0) out << "\nYpy = \n" << s.Ypy().get_k(0);
}
if( qp_eta == 1.0 ) {
omsg
<< "TangentialStepWithInequStd_Step::do_step(...) : Error, a QP relaxation parameter\n"
<< "of eta = " << qp_eta << " was calculated and therefore it must be assumed\n"
<< "that the NLP's constraints are infeasible\n"
<< "Throwing an InfeasibleConstraints exception!\n";
if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
out << omsg.str();
}
throw InfeasibleConstraints(omsg.str());
}
if( throw_qp_failure )
throw QPFailure( omsg.str(), qp_solver().get_qp_stats() );
return true;
}
bool NLPInterfacePack::test_nlp_direct(
NLPDirect *nlp
,OptionsFromStreamPack::OptionsFromStream *options
,std::ostream *out
)
{
using TestingHelperPack::update_success;
bool result;
bool success = true;
Teuchos::VerboseObjectTempState<NLP>
nlpOutputTempState(Teuchos::rcp(nlp,false),Teuchos::getFancyOStream(Teuchos::rcp(out,false)),Teuchos::VERB_LOW);
if(out)
*out
<< "\n****************************"
<< "\n*** test_nlp_direct(...) ***"
<< "\n*****************************\n";
nlp->initialize(true);
// Test the DVector spaces
if(out)
*out << "\nTesting the vector spaces ...\n";
VectorSpaceTester vec_space_tester;
if(options) {
VectorSpaceTesterSetOptions
opt_setter(&vec_space_tester);
opt_setter.set_options(*options);
}
if(out)
*out << "\nTesting nlp->space_x() ...\n";
result = vec_space_tester.check_vector_space(*nlp->space_x(),out);
if(out) {
if(result)
*out << "nlp->space_x() checks out!\n";
else
*out << "nlp->space_x() check failed!\n";
}
update_success( result, &success );
if(out)
*out << "\nTesting nlp->space_x()->sub_space(nlp->var_dep()) ...\n";
result = vec_space_tester.check_vector_space(
*nlp->space_x()->sub_space(nlp->var_dep()),out);
if(out) {
if(result)
*out << "nlp->space_x()->sub_space(nlp->var_dep()) checks out!\n";
else
*out << "nlp->space_x()->sub_space(nlp->var_dep()) check failed!\n";
}
update_success( result, &success );
if(out)
*out << "\nTesting nlp->space_x()->sub_space(nlp->var_indep()) ...\n";
result = vec_space_tester.check_vector_space(
*nlp->space_x()->sub_space(nlp->var_indep()),out);
if(out) {
if(result)
*out << "nlp->space_x()->sub_space(nlp->var_indep()) checks out!\n";
else
*out << "nlp->space_x()->sub_space(nlp->var_indep()) check failed!\n";
}
update_success( result, &success );
if(out)
*out << "\nTesting nlp->space_c() ...\n";
result = vec_space_tester.check_vector_space(*nlp->space_c(),out);
if(out) {
if(result)
*out << "nlp->space_c() checks out!\n";
else
*out << "nlp->space_c() check failed!\n";
}
update_success( result, &success );
if(out)
*out << "\nTesting nlp->space_c()->sub_space(nlp->con_decomp()) ...\n";
result = vec_space_tester.check_vector_space(
*nlp->space_c()->sub_space(nlp->con_decomp()),out);
if(out) {
if(result)
*out << "nlp->space_c()->sub_space(nlp->con_decomp()) checks out!\n";
else
*out << "nlp->space_c()->sub_space(nlp->con_decomp()) check failed!\n";
}
update_success( result, &success );
if( nlp->con_decomp().size() < nlp->m() ) {
if(out)
*out << "\nTesting nlp->space_c()->sub_space(nlp->con_undecomp()) ...\n";
result = vec_space_tester.check_vector_space(
*nlp->space_c()->sub_space(nlp->con_undecomp()),out);
if(out) {
if(result)
*out << "nlp->space_c()->sub_space(nlp->con_undecomp()) checks out!\n";
else
*out << "nlp->space_c()->sub_space(nlp->con_undecomp()) check failed!\n";
}
update_success( result, &success );
}
// Test the NLP interface first!
NLPTester nlp_tester;
if(options) {
NLPTesterSetOptions
nlp_tester_opt_setter(&nlp_tester);
nlp_tester_opt_setter.set_options(*options);
}
const bool print_all_warnings = nlp_tester.print_all();
result = nlp_tester.test_interface(
nlp, nlp->xinit(), print_all_warnings, out );
update_success( result, &success );
// Test the NLPDirect interface now!
const bool supports_Gf = nlp->supports_Gf();
if(out)
*out << "\nCalling nlp->calc_point(...) at nlp->xinit() ...\n";
const size_type
n = nlp->n(),
m = nlp->m();
const Range1D
var_dep = nlp->var_dep(),
var_indep = nlp->var_indep(),
con_decomp = nlp->con_decomp(),
con_undecomp = nlp->con_undecomp();
VectorSpace::vec_mut_ptr_t
c = nlp->space_c()->create_member(),
Gf = nlp->space_x()->create_member(),
py = nlp->space_x()->sub_space(var_dep)->create_member(),
rGf = nlp->space_x()->sub_space(var_indep)->create_member();
NLPDirect::mat_fcty_ptr_t::element_type::obj_ptr_t
GcU = con_decomp.size() < m ? nlp->factory_GcU()->create() : Teuchos::null,
D = nlp->factory_D()->create(),
Uz = con_decomp.size() < m ? nlp->factory_Uz()->create() : Teuchos::null;
nlp->calc_point(
nlp->xinit(), NULL, c.get(), true, supports_Gf?Gf.get():NULL, py.get(), rGf.get()
,GcU.get(), D.get(), Uz.get() );
if(out) {
if(supports_Gf) {
*out << "\n||Gf||inf = " << Gf->norm_inf();
if(nlp_tester.print_all())
*out << "\nGf =\n" << *Gf;
}
*out << "\n||py||inf = " << py->norm_inf();
if(nlp_tester.print_all())
*out << "\npy =\n" << *py;
*out << "\n||rGf||inf = " << rGf->norm_inf();
if(nlp_tester.print_all())
*out << "\nrGf =\n" << *rGf;
if(nlp_tester.print_all())
*out << "\nD =\n" << *D;
if( con_decomp.size() < m ) {
TEST_FOR_EXCEPT(true); // ToDo: Print GcU and Uz
}
*out << "\n";
}
CalcFiniteDiffProd
calc_fd_prod;
if(options) {
CalcFiniteDiffProdSetOptions
options_setter( &calc_fd_prod );
options_setter.set_options(*options);
}
NLPDirectTester
nlp_first_order_direct_tester(Teuchos::rcp(&calc_fd_prod,false));
if(options) {
NLPDirectTesterSetOptions
nlp_tester_opt_setter(&nlp_first_order_direct_tester);
nlp_tester_opt_setter.set_options(*options);
}
result = nlp_first_order_direct_tester.finite_diff_check(
nlp, nlp->xinit()
,nlp->num_bounded_x() ? &nlp->xl() : NULL
,nlp->num_bounded_x() ? &nlp->xu() : NULL
,c.get()
,supports_Gf?Gf.get():NULL,py.get(),rGf.get(),GcU.get(),D.get(),Uz.get()
,print_all_warnings, out
);
update_success( result, &success );
return success;
}
void SpmdMultiVectorBase<Scalar>::mvMultiReductApplyOpImpl(
const RTOpPack::RTOpT<Scalar> &pri_op,
const ArrayView<const Ptr<const MultiVectorBase<Scalar> > > &multi_vecs,
const ArrayView<const Ptr<MultiVectorBase<Scalar> > > &targ_multi_vecs,
const ArrayView<const Ptr<RTOpPack::ReductTarget> > &reduct_objs,
const Ordinal pri_global_offset_in
) const
{
using Teuchos::dyn_cast;
using Teuchos::Workspace;
Teuchos::WorkspaceStore* wss = Teuchos::get_default_workspace_store().get();
const Ordinal numCols = this->domain()->dim();
const SpmdVectorSpaceBase<Scalar> &spmdSpc = *spmdSpace();
#ifdef TEUCHOS_DEBUG
TEUCHOS_TEST_FOR_EXCEPTION(
in_applyOp_, std::invalid_argument,
"SpmdMultiVectorBase<>::mvMultiReductApplyOpImpl(...): Error, this method is"
" being entered recursively which is a clear sign that one of the methods"
" acquireDetachedView(...), releaseDetachedView(...) or commitDetachedView(...)"
" was not implemented properly!"
);
apply_op_validate_input(
"SpmdMultiVectorBase<>::mvMultiReductApplyOpImpl(...)", *this->domain(),
*this->range(), pri_op, multi_vecs, targ_multi_vecs, reduct_objs,
pri_global_offset_in);
#endif
// Flag that we are in applyOp()
in_applyOp_ = true;
// First see if this is a locally replicated vector in which case
// we treat this as a local operation only.
const bool locallyReplicated = (localSubDim_ == globalDim_);
const Range1D local_rng(localOffset_, localOffset_+localSubDim_-1);
const Range1D col_rng(0, numCols-1);
// Create sub-vector views of all of the *participating* local data
Workspace<RTOpPack::ConstSubMultiVectorView<Scalar> >
sub_multi_vecs(wss,multi_vecs.size());
Workspace<RTOpPack::SubMultiVectorView<Scalar> >
targ_sub_multi_vecs(wss,targ_multi_vecs.size());
for(int k = 0; k < multi_vecs.size(); ++k ) {
multi_vecs[k]->acquireDetachedView(local_rng, col_rng, &sub_multi_vecs[k]);
sub_multi_vecs[k].setGlobalOffset(localOffset_+pri_global_offset_in);
}
for(int k = 0; k < targ_multi_vecs.size(); ++k ) {
targ_multi_vecs[k]->acquireDetachedView(local_rng, col_rng, &targ_sub_multi_vecs[k]);
targ_sub_multi_vecs[k].setGlobalOffset(localOffset_+pri_global_offset_in);
}
Workspace<RTOpPack::ReductTarget*> reduct_objs_ptr(wss, reduct_objs.size());
for (int k = 0; k < reduct_objs.size(); ++k) {
reduct_objs_ptr[k] = &*reduct_objs[k];
}
// Apply the RTOp operator object (all processors must participate)
RTOpPack::SPMD_apply_op(
locallyReplicated ? NULL : spmdSpc.getComm().get(), // comm
pri_op, // op
col_rng.size(), // num_cols
multi_vecs.size(), // multi_vecs.size()
multi_vecs.size() ? &sub_multi_vecs[0] : NULL, // sub_multi_vecs
targ_multi_vecs.size(), // targ_multi_vecs.size()
targ_multi_vecs.size() ? &targ_sub_multi_vecs[0] : NULL, // targ_sub_multi_vecs
reduct_objs.size() ? &reduct_objs_ptr[0] : 0 // reduct_objs
);
// Free and commit the local data
for(int k = 0; k < multi_vecs.size(); ++k ) {
sub_multi_vecs[k].setGlobalOffset(local_rng.lbound());
multi_vecs[k]->releaseDetachedView( &sub_multi_vecs[k] );
}
for(int k = 0; k < targ_multi_vecs.size(); ++k ) {
targ_sub_multi_vecs[k].setGlobalOffset(local_rng.lbound());
targ_multi_vecs[k]->commitDetachedView( &targ_sub_multi_vecs[k] );
}
// Flag that we are leaving applyOp()
in_applyOp_ = false;
}
