bool DenseLinAlgPack::assert_print_nan_inf( const DVectorSlice& v
, const std::string & name, bool throw_excpt, std::ostream* out )
{
bool has_nan_or_inf = false;
bool printed_header = false;
for( DVectorSlice::const_iterator v_itr = v.begin(); v_itr != v.end(); ++v_itr ) {
if( RTOp_is_nan_inf(*v_itr) ) {
if(out) {
if(!printed_header) {
*out
<< "The vector \"" << name
<< "\" has the following NaN or Inf entries\n";
printed_header = true;
}
*out
<< name << "(" << v_itr - v.begin() + 1 << ") = "
<< *v_itr << std::endl;
}
has_nan_or_inf = true;
}
}
if( has_nan_or_inf && throw_excpt ) {
if(out)
out->flush();
std::ostringstream omsg;
omsg
<< "assert_print_nan_inf(...) : Error, the vector named "
<< name << " has at least one element which is NaN or Inf";
throw NaNInfException( omsg.str() );
}
return !has_nan_or_inf;
}
bool DenseLinAlgPack::comp(const DVectorSlice& vs1, const DVectorSlice& vs2) {
DVectorSlice::const_iterator
vs1_itr = vs1.begin(),
vs2_itr = vs2.begin();
for(; vs1_itr != vs1.end() && vs2_itr != vs2.end(); ++vs1_itr, ++vs2_itr)
if( !_comp(*vs1_itr,*vs2_itr) ) return false;
return true;
}
void MatrixSymDiagStd::Vp_StMtV(
DVectorSlice* vs_lhs, value_type alpha, BLAS_Cpp::Transp trans_rhs1
, const DVectorSlice& vs_rhs2, value_type beta) const
{
const DVectorSlice diag = this->diag();
size_type n = diag.size();
//
// y = b*y + a * op(A) * x
//
DenseLinAlgPack::Vp_MtV_assert_sizes(
vs_lhs->size(), n, n, trans_rhs1, vs_rhs2.size() );
//
// A is symmetric and diagonal A = diag(diag) so:
//
// y(j) += a * diag(j) * x(j), for j = 1...n
//
if( vs_rhs2.stride() == 1 && vs_lhs->stride() == 1 ) {
// Optimized implementation
const value_type
*d_itr = diag.raw_ptr(),
*x_itr = vs_rhs2.raw_ptr();
value_type
*y_itr = vs_lhs->raw_ptr(),
*y_end = y_itr + vs_lhs->size();
if( beta == 0.0 ) {
while( y_itr != y_end )
*y_itr++ = alpha * (*d_itr++) * (*x_itr++);
}
else if( beta == 1.0 ) {
while( y_itr != y_end )
*y_itr++ += alpha * (*d_itr++) * (*x_itr++);
}
else {
for( ; y_itr != y_end; ++y_itr )
*y_itr = beta * (*y_itr) + alpha * (*d_itr++) * (*x_itr++);
}
}
else {
// Generic implementation
DVectorSlice::const_iterator
d_itr = diag.begin(),
x_itr = vs_rhs2.begin();
DVectorSlice::iterator
y_itr = vs_lhs->begin(),
y_end = vs_lhs->end();
for( ; y_itr != y_end; ++y_itr, ++d_itr, ++x_itr ) {
#ifdef LINALGPACK_CHECK_RANGE
TEST_FOR_EXCEPT( !( d_itr < diag.end() ) );
TEST_FOR_EXCEPT( !( x_itr < vs_rhs2.end() ) );
TEST_FOR_EXCEPT( !( y_itr < vs_lhs->end() ) );
#endif
*y_itr = beta * (*y_itr) + alpha * (*d_itr) * (*x_itr);
}
}
}
bool DenseLinAlgPack::comp_less(const DVectorSlice& vs, value_type alpha)
{
DVectorSlice::const_iterator vs_itr = vs.begin();
const value_type denom = my_max( ::fabs(alpha), 1.0 );
for(; vs_itr != vs.end(); ++vs_itr)
if( *vs_itr > alpha ) return false;
return true;
}
void MatrixSymDiagStd::V_InvMtV(
DVectorSlice* vs_lhs, BLAS_Cpp::Transp trans_rhs1
, const DVectorSlice& vs_rhs2) const
{
const DVectorSlice diag = this->diag();
size_type n = diag.size();
// y = inv(op(A)) * x
//
// A is symmetric and diagonal (A = diag(diag)) so:
//
// y(j) = x(j) / diag(j), for j = 1...n
DenseLinAlgPack::Vp_MtV_assert_sizes( vs_lhs->size()
, n, n, trans_rhs1, vs_rhs2.size() );
if( vs_rhs2.stride() == 1 && vs_lhs->stride() == 1 ) {
// Optimized implementation
const value_type
*d_itr = diag.raw_ptr(),
*x_itr = vs_rhs2.raw_ptr();
value_type
*y_itr = vs_lhs->raw_ptr(),
*y_end = y_itr + vs_lhs->size();
while( y_itr != y_end )
*y_itr++ = (*x_itr++) / (*d_itr++);
}
else {
// Generic implementation
DVectorSlice::const_iterator
d_itr = diag.begin(),
x_itr = vs_rhs2.begin();
DVectorSlice::iterator
y_itr = vs_lhs->begin(),
y_end = vs_lhs->end();
for( ; y_itr != y_end; ++y_itr, ++d_itr, ++x_itr ) {
TEST_FOR_EXCEPT( !( d_itr < diag.end() ) );
TEST_FOR_EXCEPT( !( x_itr < vs_rhs2.end() ) );
TEST_FOR_EXCEPT( !( y_itr < vs_lhs->end() ) );
*y_itr = (*x_itr)/(*d_itr);
}
}
}
void MatrixSymPosDefLBFGS::Vp_DtV( DVectorSlice* y, const DVectorSlice& x ) const
{
DenseLinAlgPack::Vp_MtV_assert_sizes(
y->dim(), m_bar_, m_bar_, BLAS_Cpp::no_trans, x.dim() );
DVectorSlice::const_iterator
d_itr = STY_.diag(0).begin(),
x_itr = x.begin();
DVectorSlice::iterator
y_itr = y->begin();
while( y_itr != y->end() )
*y_itr++ += (*d_itr++) * (*x_itr++);
}
void AbstractLinAlgPack::add_elements( SpVector* sv_lhs, value_type alpha, const DVectorSlice& vs_rhs
, size_type offset, bool add_zeros )
{
typedef SpVector::element_type ele_t;
const bool assume_sorted = !sv_lhs->nz() || ( sv_lhs->nz() && sv_lhs->is_sorted() );
DVectorSlice::const_iterator
itr = vs_rhs.begin();
if(add_zeros) {
for( size_type i = 1; i <= vs_rhs.dim(); ++i )
sv_lhs->add_element( ele_t( i + offset, alpha * (*itr++) ) );
}
else {
for( size_type i = 1; i <= vs_rhs.dim(); ++i, ++itr )
if( *itr != 0.0 )
sv_lhs->add_element( ele_t( i + offset, alpha * (*itr) ) );
}
sv_lhs->assume_sorted(assume_sorted);
}
QPSolverStats::ESolutionType
QPSolverRelaxedLOQO::imp_solve_qp(
std::ostream* out, EOutputLevel olevel, ERunTests test_what
, const DVectorSlice& g, const MatrixOp& G
, value_type etaL
, const SpVectorSlice& dL, const SpVectorSlice& dU
, const MatrixOp* E, BLAS_Cpp::Transp trans_E, const DVectorSlice* b
, const SpVectorSlice* eL, const SpVectorSlice* eU
, const MatrixOp* F, BLAS_Cpp::Transp trans_F, const DVectorSlice* f
, value_type* obj_d
, value_type* eta, DVectorSlice* d
, SpVector* nu
, SpVector* mu, DVectorSlice* Ed
, DVectorSlice* lambda, DVectorSlice* Fd
)
{
using Teuchos::Workspace;
Teuchos::WorkspaceStore* wss = wsp::default_workspace_store.get();
const value_type inf_bnd = std::numeric_limits<value_type>::max();
// const value_type real_big = 1e+20;
const value_type real_big = HUGE_VAL;
const size_type
nd = g.size(),
m_in = E ? b->size() : 0,
m_eq = F ? f->size() : 0;
//
// Create a LOQO QP definition struct
//
LOQO *loqo_lp = openlp();
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp ) );
//
// Setup loqo_r and loqo_b and count the number of actual
// constraints.
//
// LOQO's b vector storage
MALLOC( loqo_lp->b, m_in+m_eq, double ); // May not use all of this storage
DVectorSlice loqo_b( loqo_lp->b, m_in+m_eq );
// LOQO's r vector storage
MALLOC( loqo_lp->r, m_in+m_eq, double ); // May not use all of this storage
DVectorSlice loqo_r( loqo_lp->r, m_in+m_eq );
// Gives status of b.
// / j : if eL(j) > -inf_bnd
// loqo_b_stat(k) = |
// \ -j : if eL(j) <= -inf_bnd && eU(j) < +inf_bnd
//
// , for k = 1...num_inequal
//
Workspace<int> loqo_b_stat_ws(wss,m_in); // May not use all of this
DenseLinAlgPack::VectorSliceTmpl<int> loqo_b_stat(&loqo_b_stat_ws[0],loqo_b_stat_ws.size());
std::fill( loqo_b_stat.begin(), loqo_b_stat.end(), 0 ); // Initialize to zero
// Fill up loqo_b, loqo_r and loqo_b_stat
size_type num_inequal = 0; // The actual number of bouned general inequalities
if(E) {
// Read iterators
AbstractLinAlgPack::sparse_bounds_itr
eLU_itr( eL->begin(), eL->end(), eL->offset()
, eU->begin(), eU->end(), eU->offset(), inf_bnd );
// written iterators
DVectorSlice::iterator
b_itr = loqo_b.begin(),
r_itr = loqo_r.begin();
DenseLinAlgPack::VectorSliceTmpl<int>::iterator
b_stat_itr = loqo_b_stat.begin();
// loop
for( int k = 1; !eLU_itr.at_end(); ++k, ++eLU_itr, ++b_itr, ++r_itr, ++b_stat_itr, ++num_inequal )
{
const size_type j = eLU_itr.indice();
if(eLU_itr.lbound() > -inf_bnd) {
*b_itr = eLU_itr.lbound();
*r_itr = eLU_itr.ubound() >= inf_bnd ? real_big : eLU_itr.ubound() - eLU_itr.lbound();
*b_stat_itr = j; // We need to make A(k,:) = [ +op(E)(j,:), -b(j) ]
}
else {
TEUCHOS_TEST_FOR_EXCEPT( !( eLU_itr.ubound() < +inf_bnd ) );
*b_itr = -eLU_itr.ubound();
*r_itr = eLU_itr.lbound() <= -inf_bnd ? real_big : - eLU_itr.lbound() + eLU_itr.ubound();
*b_stat_itr = -j; // We need to make A(k,:) = [ -op(E)(j,:), +b(j) ]
}
}
}
if(F) {
LinAlgOpPack::V_StV( &loqo_b(num_inequal+1,num_inequal+m_eq), -1.0, *f );
loqo_r(num_inequal+1,num_inequal+m_eq) = 0.0;
}
//
// Setup the QP dimensions
//
loqo_lp->n = nd+1;
loqo_lp->m = num_inequal + m_eq;
//
// Setup loqo_c, loqo_l and loqo_u
//
// LOQO's c vector storage
MALLOC( loqo_lp->c, nd+1, double );
DVectorSlice loqo_c( loqo_lp->c, nd+1 );
loqo_c(1,nd) = g;
loqo_c(nd+1) = bigM();
// LOQO's l vector storage
MALLOC( loqo_lp->l, nd+1, double );
DVectorSlice loqo_l( loqo_lp->l, nd+1 );
std::fill( loqo_l.begin(), loqo_l.end(), -real_big );
{
SpVectorSlice::const_iterator
dL_itr = dL.begin(),
dL_end = dL.end();
for( ; dL_itr != dL_end; ++dL_itr )
loqo_l( dL_itr->indice() + dL.offset() ) = dL_itr->value();
}
loqo_l(nd+1) = etaL;
// LOQO's u vector storage
MALLOC( loqo_lp->u, nd+1, double );
DVectorSlice loqo_u( loqo_lp->u, nd+1 );
std::fill( loqo_u.begin(), loqo_u.end(), +real_big );
{
SpVectorSlice::const_iterator
dU_itr = dU.begin(),
dU_end = dU.end();
for( ; dU_itr != dU_end; ++dU_itr )
loqo_u( dU_itr->indice() + dU.offset() ) = dU_itr->value();
}
loqo_u(nd+1) = +real_big;
//
// Setup the objective and constraint matrices (using strategy interface).
//
init_hess_jacob().init_hess_jacob(
G,bigM(),E,trans_E,b,&loqo_b_stat[0],num_inequal,F,trans_F,f
,loqo_lp);
//
// Setup the starting point
//
MALLOC( loqo_lp->x, nd+1, double );
DVectorSlice loqo_x( loqo_lp->x, nd+1 );
loqo_x(1,nd) = *d;
loqo_x(nd+1) = *eta;
//
// Set some control parameters
//
// strcpy( loqo_lp->name, "loqo_qp" );
loqo_lp->quadratic = 1;
loqo_lp->convex = 1;
switch( olevel ) {
case PRINT_NONE:
loqo_lp->verbose = 0;
break;
case PRINT_BASIC_INFO:
loqo_lp->verbose = 1;
break;
case PRINT_ITER_SUMMARY:
loqo_lp->verbose = 2;
break;
case PRINT_ITER_STEPS:
loqo_lp->verbose = 3;
break;
case PRINT_ITER_ACT_SET:
loqo_lp->verbose = 4;
break;
case PRINT_ITER_VECTORS:
loqo_lp->verbose = 5;
break;
case PRINT_EVERY_THING:
loqo_lp->verbose = 6;
break;
default:
TEUCHOS_TEST_FOR_EXCEPT(true);
}
//
// Solve the QP
//
if( out && olevel >= PRINT_BASIC_INFO ) {
*out << "\nSolving QP using LOQO ...\n";
out->flush();
}
const int loqo_status = solvelp(loqo_lp);
if( out && olevel >= PRINT_BASIC_INFO ) {
*out << "\nLOQO returned status = " << loqo_status << "\n";
}
//
// Map the solution to the output arguments
//
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->x ) );
DVectorSlice loqo_x_sol( loqo_lp->x, nd+1 );
// d
*d = loqo_x_sol(1,nd);
// eta
*eta = loqo_x_sol(nd+1);
// obj_d
if(obj_d)
*obj_d = loqo_lp->primal_obj - (*eta + 0.5 * (*eta)*(*eta)) * bigM();
// nu
if(nu) {
nu->resize(nd,nd);
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->z ) );
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->s ) );
const DVectorSlice
loqo_z(loqo_lp->z,loqo_lp->n), // Multipliers for l - x <= 0
loqo_s(loqo_lp->s,loqo_lp->n); // Multipliers for x - u <= 0
DVectorSlice::const_iterator
z_itr = loqo_z.begin(),
s_itr = loqo_s.begin();
typedef SpVector::element_type ele_t;
for( size_type i = 1; i <= nd; ++i, ++z_itr, ++s_itr ) {
if( *z_itr > *s_itr && *z_itr >= nonbinding_lag_mult() ) {
// Lower bound is active
nu->add_element(ele_t(i,-(*z_itr)));
}
else if( *s_itr > *z_itr && *s_itr >= nonbinding_lag_mult() ) {
// Upper bound is active
nu->add_element(ele_t(i,+(*s_itr)));
}
}
// We could look at z(nd+1) and s(nd+1) for the value of kappa?
nu->assume_sorted(true);
}
// mu
if(mu) {
mu->resize(m_in,num_inequal);
DenseLinAlgPack::VectorSliceTmpl<int>::iterator
b_stat_itr = loqo_b_stat.begin();
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->v ) );
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->q ) );
const DVectorSlice
loqo_v(loqo_lp->v,loqo_lp->m), // Multipliers for b <= A*x
loqo_q(loqo_lp->q,loqo_lp->m); // Multipliers for A*x <= b + r
DVectorSlice::const_iterator
v_itr = loqo_v.begin(),
q_itr = loqo_q.begin();
// loop
typedef SpVector::element_type ele_t;
for( size_type k = 1; k <= num_inequal; ++k, ++b_stat_itr, ++v_itr, ++q_itr ) {
const int j = *b_stat_itr;
if( *v_itr > *q_itr && *v_itr >= nonbinding_lag_mult() ) {
// Lower bound is active
if( j < 0 ) // We had to flip this since it was really and upper bound
mu->add_element(ele_t(-j,+(*v_itr)));
else // This really was a lower bound
mu->add_element(ele_t(+j,-(*v_itr)));
}
else if( *q_itr > *v_itr && *q_itr >= nonbinding_lag_mult() ) {
// Upper bound is active
mu->add_element(ele_t(+j,+(*q_itr)));
}
}
}
// Ed
if(Ed) {
LinAlgOpPack::V_MtV( Ed, *E, trans_E, *d );
}
// lambda
if(lambda) {
TEUCHOS_TEST_FOR_EXCEPT( !( loqo_lp->y ) );
const DVectorSlice
loqo_y(loqo_lp->y,loqo_lp->m); // Multipliers for equalities
DVectorSlice::const_iterator
y_itr = loqo_y.begin() + num_inequal; // Get iterators to equalities
DVectorSlice::iterator
lambda_itr = lambda->begin();
// loop
for( size_type k = 1; k <= m_eq; ++k, ++y_itr, ++lambda_itr ) {
*lambda_itr = -(*y_itr);
}
}
// Fd
if(Fd) {
LinAlgOpPack::V_MtV( Fd, *F, trans_F, *d );
}
//
// Setup the QP statistics
//
QPSolverStats::ESolutionType solution_type = QPSolverStats::OPTIMAL_SOLUTION; // Assume this?
switch( loqo_status ) { // I had to find this out by trial and error!
case 0:
solution_type = QPSolverStats::OPTIMAL_SOLUTION;
break;
case 2:
solution_type = QPSolverStats::DUAL_FEASIBLE_POINT;
break;
default:
TEUCHOS_TEST_FOR_EXCEPT(true);
}
qp_stats_.set_stats(
solution_type, QPSolverStats::CONVEX
,loqo_lp->iter, QPSolverStats::NOT_KNOWN, QPSolverStats::NOT_KNOWN
,false, *eta > 0.0 );
//
// Clean up dynamically allocated memory for LOQO
//
inv_clo(); // frees memory associated with matrix factorization
closelp(loqo_lp); // frees all allocated arrays with free(...).
return qp_stats_.solution_type();
}
bool DenseLinAlgPack::comp(const DVectorSlice& vs, value_type alpha) {
DVectorSlice::const_iterator vs_itr = vs.begin();
for(; vs_itr != vs.end(); ++vs_itr)
if( !_comp(*vs_itr,alpha) ) return false;
return true;
}
bool MeritFunc_PenaltyParamsUpdateWithMult_AddedStep::do_step(Algorithm& _algo
, poss_type step_poss, IterationPack::EDoStepType type
, poss_type assoc_step_poss)
{
using DenseLinAlgPack::norm_inf;
NLPAlgo &algo = rsqp_algo(_algo);
NLPAlgoState &s = algo.rsqp_state();
EJournalOutputLevel olevel = algo.algo_cntr().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 );
}
MeritFuncPenaltyParams
*params = dynamic_cast<MeritFuncPenaltyParams*>(&merit_func());
if( !params ) {
std::ostringstream omsg;
omsg
<< "MeritFunc_PenaltyParamsUpdateWithMult_AddedStep::do_step(...), Error "
<< "The class " << typeName(&merit_func()) << " does not support the "
<< "MeritFuncPenaltyParams iterface\n";
out << omsg.str();
throw std::logic_error( omsg.str() );
}
MeritFuncNLPDirecDeriv
*direc_deriv = dynamic_cast<MeritFuncNLPDirecDeriv*>(&merit_func());
if( !direc_deriv ) {
std::ostringstream omsg;
omsg
<< "MeritFunc_PenaltyParamsUpdateWithMult_AddedStep::do_step(...), Error "
<< "The class " << typeName(&merit_func()) << " does not support the "
<< "MeritFuncNLPDirecDeriv iterface\n";
out << omsg.str();
throw std::logic_error( omsg.str() );
}
bool perform_update = true;
if( s.mu().updated_k(0) ) {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nmu_k is already updated by someone else?\n";
}
const value_type mu_k = s.mu().get_k(0);
if( mu_k == norm_inf_mu_last_ ) {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nmu_k " << mu_k << " == norm_inf_mu_last = " << norm_inf_mu_last_
<< "\nso we will take this as a signal to skip the update.\n";
}
perform_update = false;
}
else {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nmu_k " << mu_k << " != norm_inf_mu_last = " << norm_inf_mu_last_
<< "\nso we will ignore this and perform the update anyway.\n";
}
}
}
if(perform_update) {
if ( s.lambda().updated_k(0) ) {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nUpdate the penalty parameter...\n";
}
const DVector
&lambda_k = s.lambda().get_k(0).cv();
if( params->mu().size() != lambda_k.size() )
params->resize( lambda_k.size() );
DVectorSlice
mu = params->mu();
const value_type
max_lambda = norm_inf( lambda_k() ),
mult_fact = (1.0 + mult_factor_);
if(near_solution_) {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nNear solution, forcing mu(j) >= mu_old(j)...\n";
}
DVector::const_iterator lb_itr = lambda_k.begin();
DVectorSlice::iterator mu_itr = mu.begin();
for( ; lb_itr != lambda_k.end(); ++mu_itr, ++ lb_itr )
*mu_itr = max( max( *mu_itr, mult_fact * ::fabs(*lb_itr) ), small_mu_ );
}
else {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nNot near solution, allowing reduction in mu(j) ...\n";
}
DVector::const_iterator lb_itr = lambda_k.begin();
DVectorSlice::iterator mu_itr = mu.begin();
for( ; lb_itr != lambda_k.end(); ++mu_itr, ++ lb_itr ) {
const value_type lb_j = ::fabs(*lb_itr);
*mu_itr = max(
(3.0 * (*mu_itr) + lb_j) / 4.0
, max( mult_fact * lb_j, small_mu_ )
);
}
value_type kkt_error = s.opt_kkt_err().get_k(0) + s.feas_kkt_err().get_k(0);
if(kkt_error <= kkt_near_sol_) {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nkkt_error = " << kkt_error << " <= kkt_near_sol = "
<< kkt_near_sol_ << std::endl
<< "Switching to forcing mu_k >= mu_km1 in the future\n";
}
near_solution_ = true;
}
}
// Force the ratio
const value_type
max_mu = norm_inf( mu() ),
min_mu = min_mu_ratio_ * max_mu;
for(DVectorSlice::iterator mu_itr = mu.begin(); mu_itr != mu.end(); ++mu_itr)
*mu_itr = max( (*mu_itr), min_mu );
s.mu().set_k(0) = norm_inf_mu_last_ = max_mu;
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nmax(|mu(j)|) = " << (*std::max_element( mu.begin(), mu.end() ))
<< "\nmin(|mu(j)|) = " << (*std::min_element( mu.begin(), mu.end() ))
<< std::endl;
}
if( (int)olevel >= (int)PRINT_VECTORS ) {
out << "\nmu = \n" << mu;
}
}
else {
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nDon't have the info to update penalty parameter so just use the last updated...\n";
}
}
}
// In addition also compute the directional derivative
direc_deriv->calc_deriv( s.Gf().get_k(0)(), s.c().get_k(0)(), s.d().get_k(0)() );
if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
out << "\nmu_k = " << s.mu().get_k(0) << "\n";
}
return true;
}
