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IpTNLPAdapter.cpp
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IpTNLPAdapter.cpp
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// Copyright (C) 2004, 2010 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// Authors: Carl Laird, Andreas Waechter IBM 2004-08-13
#include "IpoptConfig.h"
#include "IpTNLPAdapter.hpp"
#include "IpBlas.hpp"
#include "IpIpoptData.hpp"
#include "IpIpoptCalculatedQuantities.hpp"
#include "IpDenseVector.hpp"
#include "IpExpansionMatrix.hpp"
#include "IpGenTMatrix.hpp"
#include "IpSymTMatrix.hpp"
#include "IpTDependencyDetector.hpp"
#include "IpTSymDependencyDetector.hpp"
#include "IpTripletToCSRConverter.hpp"
#ifdef IPOPT_HAS_HSL
#include "CoinHslConfig.h"
#include "IpMa28TDependencyDetector.hpp"
#endif
#ifdef IPOPT_HAS_MUMPS
# include "IpMumpsSolverInterface.hpp"
#endif
#ifdef IPOPT_HAS_WSMP
# include "IpWsmpSolverInterface.hpp"
#endif
#include <cmath>
#include <cstdio>
#include <cstring>
namespace Ipopt
{
#if IPOPT_VERBOSITY > 0
static const Index dbg_verbosity = 0;
#endif
TNLPAdapter::TNLPAdapter(
const SmartPtr<TNLP> tnlp,
const SmartPtr<const Journalist> jnlst /* = NULL */
)
: tnlp_(tnlp),
jnlst_(jnlst),
full_x_(NULL),
full_lambda_(NULL),
full_g_(NULL),
jac_g_(NULL),
c_rhs_(NULL),
x_tag_for_iterates_(0),
y_c_tag_for_iterates_(0),
y_d_tag_for_iterates_(0),
x_tag_for_g_(0),
x_tag_for_jac_g_(0),
jac_idx_map_(NULL),
h_idx_map_(NULL),
x_fixed_map_(NULL),
findiff_jac_ia_(NULL),
findiff_jac_ja_(NULL),
findiff_jac_postriplet_(NULL),
findiff_x_l_(NULL),
findiff_x_u_(NULL)
{
ASSERT_EXCEPTION(IsValid(tnlp_), INVALID_TNLP, "The TNLP passed to TNLPAdapter is NULL. This MUST be a valid TNLP!");
}
TNLPAdapter::~TNLPAdapter()
{
delete[] full_x_;
delete[] full_lambda_;
delete[] full_g_;
delete[] jac_g_;
delete[] c_rhs_;
delete[] jac_idx_map_;
delete[] h_idx_map_;
delete[] x_fixed_map_;
delete[] findiff_jac_ia_;
delete[] findiff_jac_ja_;
delete[] findiff_jac_postriplet_;
delete[] findiff_x_l_;
delete[] findiff_x_u_;
}
void TNLPAdapter::RegisterOptions(
SmartPtr<RegisteredOptions> roptions
)
{
roptions->SetRegisteringCategory("NLP");
roptions->AddNumberOption(
"nlp_lower_bound_inf",
"any bound less or equal this value will be considered -inf (i.e. not lower bounded).",
-1e19);
roptions->AddNumberOption(
"nlp_upper_bound_inf",
"any bound greater or this value will be considered +inf (i.e. not upper bounded).",
1e19);
roptions->AddStringOption4(
"fixed_variable_treatment",
"Determines how fixed variables should be handled.",
"make_parameter",
"make_parameter", "Remove fixed variable from optimization variables",
"make_parameter_nodual", "Remove fixed variable from optimization variables and do not compute bound multipliers for fixed variables",
"make_constraint", "Add equality constraints fixing variables",
"relax_bounds", "Relax fixing bound constraints",
"The main difference between those options is that the starting point in the \"make_constraint\" case still "
"has the fixed variables at their given values, whereas in the case \"make_parameter(_nodual)\" the functions are always "
"evaluated with the fixed values for those variables. "
"Also, for \"relax_bounds\", the fixing bound constraints are relaxed (according to\" bound_relax_factor\"). "
"For all but \"make_parameter_nodual\", bound multipliers are computed for the fixed variables.");
std::vector<std::string> options;
std::vector<std::string> descrs;
options.push_back("none");
descrs.push_back("don't check; no extra work at beginning");
#ifdef IPOPT_HAS_MUMPS
options.push_back("mumps");
descrs.push_back("use MUMPS");
#endif
#ifdef IPOPT_HAS_WSMP
options.push_back("wsmp");
descrs.push_back("use WSMP");
#endif
#if ((defined(COINHSL_HAS_MA28) && !defined(IPOPT_SINGLE)) || (defined(COINHSL_HAS_MA28S) && defined(IPOPT_SINGLE))) && defined(F77_FUNC) && !defined(IPOPT_INT64)
options.push_back("ma28");
descrs.push_back("use MA28");
#endif
roptions->AddStringOption(
"dependency_detector",
"Indicates which linear solver should be used to detect linearly dependent equality constraints.",
"none",
options,
descrs,
"This is experimental and does not work well.",
true);
roptions->AddBoolOption(
"dependency_detection_with_rhs",
"Indicates if the right hand sides of the constraints should be considered in addition to gradients during dependency detection",
false,
"",
true);
roptions->AddLowerBoundedIntegerOption(
"num_linear_variables",
"Number of linear variables",
0,
0,
"When the Hessian is approximated, it is assumed that the first num_linear_variables variables are linear. "
"The Hessian is then not approximated in this space. "
"If the get_number_of_nonlinear_variables method in the TNLP is implemented, this option is ignored.",
true);
roptions->AddStringOption2(
"jacobian_approximation",
"Specifies technique to compute constraint Jacobian",
"exact",
"exact", "user-provided derivatives",
"finite-difference-values", "user-provided structure, values by finite differences",
"",
true);
roptions->AddStringOption2(
"gradient_approximation",
"Specifies technique to compute objective Gradient",
"exact",
"exact", "user-provided gradient",
"finite-difference-values", "values by finite differences",
"",
true);
roptions->AddLowerBoundedNumberOption(
"findiff_perturbation",
"Size of the finite difference perturbation for derivative approximation.",
0., true,
1e-7,
"This determines the relative perturbation of the variable entries.",
true);
roptions->SetRegisteringCategory("Derivative Checker");
roptions->AddStringOption4(
"derivative_test",
"Enable derivative checker",
"none",
"none", "do not perform derivative test",
"first-order", "perform test of first derivatives at starting point",
"second-order", "perform test of first and second derivatives at starting point",
"only-second-order", "perform test of second derivatives at starting point",
"If this option is enabled, a (slow!) derivative test will be performed before the optimization. "
"The test is performed at the user provided starting point and marks derivative values that seem suspicious");
roptions->AddLowerBoundedIntegerOption(
"derivative_test_first_index",
"Index of first quantity to be checked by derivative checker",
-2,
-2,
"If this is set to -2, then all derivatives are checked. "
"Otherwise, for the first derivative test it specifies the first variable for which the test is done (counting starts at 0). "
"For second derivatives, it specifies the first constraint for which the test is done; "
"counting of constraint indices starts at 0, and -1 refers to the objective function Hessian.");
roptions->AddLowerBoundedNumberOption(
"derivative_test_perturbation",
"Size of the finite difference perturbation in derivative test.",
0., true,
1e-8,
"This determines the relative perturbation of the variable entries.");
roptions->AddLowerBoundedNumberOption(
"derivative_test_tol",
"Threshold for indicating wrong derivative.",
0., true,
1e-4,
"If the relative deviation of the estimated derivative from the given one is larger than this value, "
"the corresponding derivative is marked as wrong.");
roptions->AddBoolOption(
"derivative_test_print_all",
"Indicates whether information for all estimated derivatives should be printed.",
false,
"Determines verbosity of derivative checker.");
roptions->AddLowerBoundedNumberOption(
"point_perturbation_radius",
"Maximal perturbation of an evaluation point.",
0., false,
10.,
"If a random perturbation of a points is required, this number indicates the maximal perturbation. "
"This is for example used when determining the center point at which the finite difference derivative test is executed.");
}
bool TNLPAdapter::ProcessOptions(
const OptionsList& options,
const std::string& prefix
)
{
DBG_START_METH("TNLPAdapter::ProcessOptions", dbg_verbosity);
options.GetNumericValue("nlp_lower_bound_inf", nlp_lower_bound_inf_, prefix);
options.GetNumericValue("nlp_upper_bound_inf", nlp_upper_bound_inf_, prefix);
ASSERT_EXCEPTION(nlp_lower_bound_inf_ < nlp_upper_bound_inf_, OPTION_INVALID,
"Option \"nlp_lower_bound_inf\" must be smaller than \"nlp_upper_bound_inf\".");
// Registered in IpOrigIpoptNLP
options.GetNumericValue("bound_relax_factor", bound_relax_factor_, prefix);
Index enum_int;
options.GetEnumValue("fixed_variable_treatment", enum_int, prefix);
fixed_variable_treatment_ = FixedVariableTreatmentEnum(enum_int);
options.GetEnumValue("derivative_test", enum_int, prefix);
derivative_test_ = DerivativeTestEnum(enum_int);
options.GetNumericValue("derivative_test_perturbation", derivative_test_perturbation_, prefix);
options.GetNumericValue("derivative_test_tol", derivative_test_tol_, prefix);
options.GetBoolValue("derivative_test_print_all", derivative_test_print_all_, prefix);
options.GetIntegerValue("derivative_test_first_index", derivative_test_first_index_, prefix);
// The option warm_start_same_structure is registered by OrigIpoptNLP
options.GetBoolValue("warm_start_same_structure", warm_start_same_structure_, prefix);
// The following is registered in OrigIpoptNLP
options.GetEnumValue("hessian_approximation", enum_int, prefix);
hessian_approximation_ = HessianApproximationType(enum_int);
options.GetIntegerValue("num_linear_variables", num_linear_variables_, prefix);
options.GetEnumValue("jacobian_approximation", enum_int, prefix);
jacobian_approximation_ = JacobianApproxEnum(enum_int);
options.GetEnumValue("gradient_approximation", enum_int, prefix);
gradient_approximation_ = GradientApproxEnum(enum_int);
options.GetNumericValue("findiff_perturbation", findiff_perturbation_, prefix);
options.GetNumericValue("point_perturbation_radius", point_perturbation_radius_, prefix);
options.GetNumericValue("tol", tol_, prefix);
options.GetBoolValue("dependency_detection_with_rhs", dependency_detection_with_rhs_, prefix);
std::string dependency_detector;
options.GetStringValue("dependency_detector", dependency_detector, prefix);
#ifdef IPOPT_HAS_MUMPS
if( dependency_detector == "mumps" )
{
SmartPtr<SparseSymLinearSolverInterface> SolverInterface;
SolverInterface = new MumpsSolverInterface();
SmartPtr<TSymLinearSolver> ScaledSolver =
new TSymLinearSolver(SolverInterface, NULL);
dependency_detector_ = new TSymDependencyDetector(*ScaledSolver);
}
#endif
#ifdef IPOPT_HAS_WSMP
if( dependency_detector == "wsmp" )
{
SmartPtr<SparseSymLinearSolverInterface> SolverInterface;
#ifdef PARDISO_MATCHING_PREPROCESS
std::string libname;
options.GetStringValue("pardisolib", libname, prefix);
SolverInterface = new WsmpSolverInterface(new LibraryLoader(libname));
#else
SolverInterface = new WsmpSolverInterface();
#endif
SmartPtr<TSymLinearSolver> ScaledSolver =
new TSymLinearSolver(SolverInterface, NULL);
dependency_detector_ = new TSymDependencyDetector(*ScaledSolver);
}
#endif
#if ((defined(COINHSL_HAS_MA28) && !defined(IPOPT_SINGLE)) || (defined(COINHSL_HAS_MA28S) && defined(IPOPT_SINGLE))) && defined(F77_FUNC) && !defined(IPOPT_INT64)
if( dependency_detector == "ma28" )
{
dependency_detector_ = new Ma28TDependencyDetector();
}
#endif
if( IsValid(dependency_detector_) )
if( !dependency_detector_->ReducedInitialize(*jnlst_, options, prefix) )
{
return false;
}
return true;
}
bool TNLPAdapter::GetSpaces(
SmartPtr<const VectorSpace>& x_space,
SmartPtr<const VectorSpace>& c_space,
SmartPtr<const VectorSpace>& d_space,
SmartPtr<const VectorSpace>& x_l_space,
SmartPtr<const MatrixSpace>& px_l_space,
SmartPtr<const VectorSpace>& x_u_space,
SmartPtr<const MatrixSpace>& px_u_space,
SmartPtr<const VectorSpace>& d_l_space,
SmartPtr<const MatrixSpace>& pd_l_space,
SmartPtr<const VectorSpace>& d_u_space,
SmartPtr<const MatrixSpace>& pd_u_space,
SmartPtr<const MatrixSpace>& Jac_c_space,
SmartPtr<const MatrixSpace>& Jac_d_space,
SmartPtr<const SymMatrixSpace>& Hess_lagrangian_space
)
{
DBG_START_METH("TNLPAdapter::GetSpaces", dbg_verbosity);
// First, if required, perform derivative test
if( derivative_test_ != NO_TEST )
{
bool retval = CheckDerivatives(derivative_test_, derivative_test_first_index_);
if( !retval )
{
return retval;
}
}
if( warm_start_same_structure_ )
{
ASSERT_EXCEPTION(full_x_, INVALID_WARMSTART,
"warm_start_same_structure chosen, but TNLPAdapter is called for the first time.");
if( IsValid(jnlst_) )
{
jnlst_->Printf(J_DETAILED, J_INITIALIZATION, "Reusing previous information for warm start in TNLPAdapter.\n");
}
}
else
{
// In case the Adapter has been used before, but this is not a
// warm start, make sure we delete all previously allocated
// memory
delete[] full_x_;
full_x_ = NULL;
delete[] full_lambda_;
full_lambda_ = NULL;
delete[] full_g_;
full_g_ = NULL;
delete[] jac_g_;
jac_g_ = NULL;
delete[] c_rhs_;
c_rhs_ = NULL;
delete[] jac_idx_map_;
jac_idx_map_ = NULL;
delete[] h_idx_map_;
h_idx_map_ = NULL;
delete[] x_fixed_map_;
x_fixed_map_ = NULL;
jac_fixed_idx_map_.clear();
jac_fixed_iRow_.clear();
jac_fixed_jCol_.clear();
#if __cplusplus >= 201103L
jac_fixed_idx_map_.shrink_to_fit();
jac_fixed_iRow_.shrink_to_fit();
jac_fixed_jCol_.shrink_to_fit();
#endif
}
// Get the full dimensions of the problem
Index n_full_x, n_full_g, nz_full_jac_g, nz_full_h;
bool retval = tnlp_->get_nlp_info(n_full_x, n_full_g, nz_full_jac_g, nz_full_h, index_style_);
ASSERT_EXCEPTION(retval, INVALID_TNLP, "get_nlp_info returned false");
ASSERT_EXCEPTION(n_full_x >= 0, INVALID_TNLP, "number of variables negative");
ASSERT_EXCEPTION(n_full_g >= 0, INVALID_TNLP, "number of constraints negative");
ASSERT_EXCEPTION(nz_full_jac_g >= 0, INVALID_TNLP, "number of nonzeros in Jacobian negative");
ASSERT_EXCEPTION(nz_full_h >= 0, INVALID_TNLP, "number of nonzeros in Hessian negative");
ASSERT_EXCEPTION(
!warm_start_same_structure_
|| (n_full_x == n_full_x_ && n_full_g == n_full_g_ && nz_full_jac_g == nz_full_jac_g_
&& nz_full_h == nz_full_h_), INVALID_WARMSTART,
"warm_start_same_structure chosen, but problem dimensions are different.");
n_full_x_ = n_full_x;
n_full_g_ = n_full_g;
nz_full_jac_g_ = nz_full_jac_g;
nz_full_h_ = nz_full_h;
if( !warm_start_same_structure_ )
{
// create space to store vectors that are the full length of x
full_x_ = new Number[n_full_x_];
// create space to store vectors that area the full length of lambda
full_lambda_ = new Number[n_full_g_];
// create space to store vectors that are the full length of g
full_g_ = new Number[n_full_g_];
// check if there is any meta data for the variables and constraints
StringMetaDataMapType var_string_md;
IntegerMetaDataMapType var_integer_md;
NumericMetaDataMapType var_numeric_md;
StringMetaDataMapType con_string_md;
IntegerMetaDataMapType con_integer_md;
NumericMetaDataMapType con_numeric_md;
if( !tnlp_->get_var_con_metadata(n_full_x_, var_string_md, var_integer_md, var_numeric_md, n_full_g_,
con_string_md, con_integer_md, con_numeric_md) )
{
var_string_md.clear();
var_integer_md.clear();
var_numeric_md.clear();
con_string_md.clear();
con_integer_md.clear();
con_numeric_md.clear();
}
// allocate internal space to store the full jacobian
jac_g_ = new Number[nz_full_jac_g_];
/* Spaces for bounds. We need to remove the fixed variables
* and find out which bounds do not exist. */
Number* x_l = new Number[n_full_x_];
Number* x_u = new Number[n_full_x_];
Number* g_l = new Number[n_full_g_];
Number* g_u = new Number[n_full_g_];
retval = tnlp_->get_bounds_info(n_full_x_, x_l, x_u, n_full_g_, g_l, g_u);
ASSERT_EXCEPTION(retval, INVALID_TNLP, "get_bounds_info returned false in GetSpaces");
//*********************************************************
// Create the spaces and permutation spaces
//*********************************************************
Index n_x_var;
Index n_x_l;
Index n_x_u;
Index* x_not_fixed_map = new Index[n_full_x_];
Index* x_l_map = new Index[n_full_x_];
Index* x_u_map = new Index[n_full_x_];
Index n_c;
Index n_d;
Index n_d_l;
Index n_d_u;
Index* c_map = new Index[n_full_g_]; // we do not know n_c yet!
Index* d_map = new Index[n_full_g_]; // we do not know n_d yet!
Index* d_l_map = new Index[n_full_g_]; // "
Index* d_u_map = new Index[n_full_g_]; // "
bool done = false;
// We might have to do the following twice: If we detect that we
// don't have enough degrees of freedom, we simply redo
// everything with fixed_variable_treatment to set RELAX_BOUNDS
while( !done )
{
n_x_var = 0;
n_x_l = 0;
n_x_u = 0;
n_x_fixed_ = 0;
Index* x_fixed_map_tmp = new Index[n_full_x_];
for( Index i = 0; i < n_full_x_; i++ )
{
Number lower_bound = x_l[i];
Number upper_bound = x_u[i];
if( lower_bound == upper_bound )
{
switch( fixed_variable_treatment_ )
{
case MAKE_PARAMETER:
case MAKE_PARAMETER_NODUAL:
// Variable is fixed, remove it from the problem
full_x_[i] = lower_bound;
x_fixed_map_tmp[n_x_fixed_] = i;
n_x_fixed_++;
break;
case MAKE_CONSTRAINT:
x_fixed_map_tmp[n_x_fixed_] = i; // don't really need this
// array then
n_x_fixed_++;
x_not_fixed_map[n_x_var] = i;
n_x_var++;
break;
case RELAX_BOUNDS:
x_l_map[n_x_l] = n_x_var;
n_x_l++;
x_u_map[n_x_u] = n_x_var;
n_x_u++;
n_x_var++;
break;
default:
DBG_ASSERT(false && "invalid fixed_variable_treatment_");
}
}
else if( lower_bound > upper_bound )
{
char string[128];
Snprintf(string, 127,
"There are inconsistent bounds on variable %" IPOPT_INDEX_FORMAT ": lower = %25.16e and upper = %25.16e.", i, lower_bound,
upper_bound);
delete[] x_l;
delete[] x_u;
delete[] g_l;
delete[] g_u;
delete[] x_not_fixed_map;
delete[] x_fixed_map_tmp;
delete[] x_l_map;
delete[] x_u_map;
delete[] c_map;
delete[] d_map;
delete[] d_l_map;
delete[] d_u_map;
THROW_EXCEPTION(INCONSISTENT_BOUNDS, string);
}
else
{
x_not_fixed_map[n_x_var] = i;
if( lower_bound > nlp_lower_bound_inf_ )
{
x_l_map[n_x_l] = n_x_var;
n_x_l++;
}
if( upper_bound < nlp_upper_bound_inf_ )
{
x_u_map[n_x_u] = n_x_var;
n_x_u++;
}
n_x_var++;
}
}
// If there are fixed variables, we keep their position around
// for a possible warm start later or if fixed variables are
// treated by added equality constraints
if( n_x_fixed_ > 0 )
{
delete[] x_fixed_map_;
x_fixed_map_ = NULL;
x_fixed_map_ = new Index[n_x_fixed_];
for( Index i = 0; i < n_x_fixed_; i++ )
{
x_fixed_map_[i] = x_fixed_map_tmp[i];
}
}
else
{
delete[] x_fixed_map_;
x_fixed_map_ = NULL;
}
delete[] x_fixed_map_tmp;
// Create the spaces for c and d
// - includes the internal permutation matrices for
// full_g to c and d
// - includes the permutation matrices for d_l and d_u
// c(x) = (P_c)T * g(x)
// d(x) = (P_d)T * g(x)
// d_L = (P_d_L)T * (P_d)T * g_l
// d_U = (P_d_U)T * (P_d)T * g_u
n_c = 0;
n_d = 0;
n_d_l = 0;
n_d_u = 0;
for( Index i = 0; i < n_full_g_; i++ )
{
Number lower_bound = g_l[i];
Number upper_bound = g_u[i];
if( lower_bound == upper_bound )
{
// equality constraint
c_map[n_c] = i;
n_c++;
}
else if( lower_bound > upper_bound )
{
delete[] x_l;
delete[] x_u;
delete[] g_l;
delete[] g_u;
delete[] x_not_fixed_map;
delete[] x_l_map;
delete[] x_u_map;
delete[] c_map;
delete[] d_map;
delete[] d_l_map;
delete[] d_u_map;
char string[128];
Snprintf(string, 127,
"There are inconsistent bounds on constraint function %" IPOPT_INDEX_FORMAT ": lower = %25.16e and upper = %25.16e.", i,
lower_bound, upper_bound);
THROW_EXCEPTION(INCONSISTENT_BOUNDS, string);
}
else
{
// inequality constraint
d_map[n_d] = i;
if( lower_bound > nlp_lower_bound_inf_ )
{
d_l_map[n_d_l] = n_d;
n_d_l++;
}
if( upper_bound < nlp_upper_bound_inf_ )
{
d_u_map[n_d_u] = n_d;
n_d_u++;
}
n_d++;
}
}
if( fixed_variable_treatment_ == RELAX_BOUNDS || n_x_fixed_ == 0 || n_x_var >= n_c || n_x_var == 0 )
{
done = true;
}
else
{
fixed_variable_treatment_ = RELAX_BOUNDS;
jnlst_->Printf(J_WARNING, J_INITIALIZATION,
"Too few degrees of freedom (n_x = %" IPOPT_INDEX_FORMAT ", n_c = %" IPOPT_INDEX_FORMAT ").\n Trying fixed_variable_treatment = RELAX_BOUNDS\n\n",
n_x_var, n_c);
}
} // while (!done)
// If requested, check if there are linearly dependent equality
// constraints
if( n_c > 0 && IsValid(dependency_detector_) )
{
std::list<Index> c_deps;
if( !DetermineDependentConstraints(n_x_var, x_not_fixed_map, x_l, x_u, g_l, g_u, n_c, c_map, c_deps) )
{
jnlst_->Printf(J_WARNING, J_INITIALIZATION,
"Dependent constraint detector had a problem, assume full rank.\n");
}
c_deps.sort();
if( !c_deps.empty() )
{
jnlst_->Printf(J_WARNING, J_INITIALIZATION,
"\nDetected %zd linearly dependent equality constraints; taking those out.\n\n", c_deps.size());
}
else
{
jnlst_->Printf(J_DETAILED, J_INITIALIZATION, "\nNo dependent constraints detected.\n\n");
}
if( jnlst_->ProduceOutput(J_DETAILED, J_INITIALIZATION) )
{
jnlst_->Printf(J_DETAILED, J_INITIALIZATION, "\nList of indices of dependent constraints:\n");
Index count = 0;
for( std::list<Index>::iterator i = c_deps.begin(); i != c_deps.end(); ++i )
{
jnlst_->Printf(J_DETAILED, J_INITIALIZATION, "c_dep[%" IPOPT_INDEX_FORMAT "] = %" IPOPT_INDEX_FORMAT "\n", count++, *i);
}
jnlst_->Printf(J_DETAILED, J_INITIALIZATION, "\n");
}
if( !c_deps.empty() )
{
// Take the dependent constraints out.
// We assume that the list in c_dep is sorted
std::list<Index>::iterator idep = c_deps.begin();
Index new_n_c = *idep;
for( Index i = *idep; i < n_c; i++ )
{
if( i == *idep )
{
++idep;
}
else
{
c_map[new_n_c] = c_map[i];
new_n_c++;
}
if( idep == c_deps.end() )
{
// just copy the rest and done
for( Index j = i + 1; j < n_c; j++ )
{
c_map[new_n_c++] = c_map[j];
}
break;
}
}
n_c = new_n_c;
// We also need to set the multipliers for the constraints
// to zero (could do only for dependent ones... was too lazy
// right now)
const Number zero = 0.;
IpBlasCopy(n_full_g_, &zero, 0, full_lambda_, 1);
}
}
delete[] x_l;
x_l = NULL;
delete[] x_u;
x_u = NULL;
// create x spaces
SmartPtr<DenseVectorSpace> dv_x_space = new DenseVectorSpace(n_x_var);
x_space_ = GetRawPtr(dv_x_space);
SmartPtr<DenseVectorSpace> dv_x_l_space = new DenseVectorSpace(n_x_l);
x_l_space_ = GetRawPtr(dv_x_l_space);
SmartPtr<DenseVectorSpace> dv_x_u_space = new DenseVectorSpace(n_x_u);
x_u_space_ = GetRawPtr(dv_x_u_space);
if( n_x_fixed_ > 0 && (fixed_variable_treatment_ == MAKE_PARAMETER || fixed_variable_treatment_ == MAKE_PARAMETER_NODUAL) )
{
P_x_full_x_space_ = new ExpansionMatrixSpace(n_full_x_, n_x_var, x_not_fixed_map);
P_x_full_x_ = P_x_full_x_space_->MakeNewExpansionMatrix();
}
else
{
P_x_full_x_space_ = NULL;
P_x_full_x_ = NULL;
}
P_x_x_L_space_ = new ExpansionMatrixSpace(n_x_var, n_x_l, x_l_map);
px_l_space_ = GetRawPtr(P_x_x_L_space_);
P_x_x_L_ = P_x_x_L_space_->MakeNewExpansionMatrix();
P_x_x_U_space_ = new ExpansionMatrixSpace(n_x_var, n_x_u, x_u_map);
px_u_space_ = GetRawPtr(P_x_x_U_space_);
P_x_x_U_ = P_x_x_U_space_->MakeNewExpansionMatrix();
// setup the variable meta data if present
if( var_string_md.size() > 0 )
{
for( StringMetaDataMapType::iterator iter = var_string_md.begin(); iter != var_string_md.end(); ++iter )
{
std::vector<std::string> string_md(n_x_var);
const Index* pos_idx = NULL;
if( IsValid(P_x_full_x_space_) )
{
pos_idx = P_x_full_x_->ExpandedPosIndices();
for( Index i = 0; i < n_x_var; i++ )
{
string_md[i] = iter->second[pos_idx[i]];
}
}
else
{
for( Index i = 0; i < n_x_var; i++ )
{
string_md[i] = iter->second[i];
}
}
dv_x_space->SetStringMetaData(iter->first, string_md);
string_md.clear();
string_md.resize(n_x_l);
const Index* pos_idxL = P_x_x_L_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_l; i++ )
{
if( pos_idx != NULL )
{
string_md[i] = iter->second[pos_idx[pos_idxL[i]]];
}
else
{
string_md[i] = iter->second[pos_idxL[i]];
}
}
dv_x_l_space->SetStringMetaData(iter->first, string_md);
string_md.clear();
string_md.resize(n_x_u);
const Index* pos_idxU = P_x_x_U_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_u; i++ )
{
if( pos_idx != NULL )
{
string_md[i] = iter->second[pos_idx[pos_idxU[i]]];
}
else
{
string_md[i] = iter->second[pos_idxU[i]];
}
}
dv_x_u_space->SetStringMetaData(iter->first, string_md);
}
}
if( var_integer_md.size() > 0 )
{
for( IntegerMetaDataMapType::iterator iter = var_integer_md.begin(); iter != var_integer_md.end(); ++iter )
{
std::vector<Index> integer_md(n_x_var);
const Index* pos_idx = NULL;
if( IsValid(P_x_full_x_space_) )
{
pos_idx = P_x_full_x_->ExpandedPosIndices();
for( Index i = 0; i < n_x_var; i++ )
{
integer_md[i] = iter->second[pos_idx[i]];
}
}
else
{
for( Index i = 0; i < n_x_var; i++ )
{
integer_md[i] = iter->second[i];
}
}
dv_x_space->SetIntegerMetaData(iter->first, integer_md);
integer_md.clear();
integer_md.resize(n_x_l);
const Index* pos_idxL = P_x_x_L_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_l; i++ )
{
if( pos_idx != NULL )
{
integer_md[i] = iter->second[pos_idx[pos_idxL[i]]];
}
else
{
integer_md[i] = iter->second[pos_idxL[i]];
}
}
dv_x_l_space->SetIntegerMetaData(iter->first, integer_md);
integer_md.clear();
integer_md.resize(n_x_u);
const Index* pos_idxU = P_x_x_U_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_u; i++ )
{
if( pos_idx != NULL )
{
integer_md[i] = iter->second[pos_idx[pos_idxU[i]]];
}
else
{
integer_md[i] = iter->second[pos_idxU[i]];
}
}
dv_x_u_space->SetIntegerMetaData(iter->first, integer_md);
}
}
if( var_numeric_md.size() > 0 )
{
for( NumericMetaDataMapType::iterator iter = var_numeric_md.begin(); iter != var_numeric_md.end(); ++iter )
{
std::vector<Number> numeric_md(n_x_var);
const Index* pos_idx = NULL;
if( IsValid(P_x_full_x_space_) )
{
pos_idx = P_x_full_x_->ExpandedPosIndices();
for( Index i = 0; i < n_x_var; i++ )
{
numeric_md[i] = iter->second[pos_idx[i]];
}
}
else
{
for( Index i = 0; i < n_x_var; i++ )
{
numeric_md[i] = iter->second[i];
}
}
dv_x_space->SetNumericMetaData(iter->first, numeric_md);
numeric_md.clear();
numeric_md.resize(n_x_l);
const Index* pos_idxL = P_x_x_L_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_l; i++ )
{
if( pos_idx != NULL )
{
numeric_md[i] = iter->second[pos_idx[pos_idxL[i]]];
}
else
{
numeric_md[i] = iter->second[pos_idxL[i]];
}
}
dv_x_l_space->SetNumericMetaData(iter->first, numeric_md);
numeric_md.clear();
numeric_md.resize(n_x_u);
const Index* pos_idxU = P_x_x_U_space_->ExpandedPosIndices();
for( Index i = 0; i < n_x_u; i++ )
{
if( pos_idx != NULL )
{
numeric_md[i] = iter->second[pos_idx[pos_idxU[i]]];
}
else
{
numeric_md[i] = iter->second[pos_idxU[i]];
}
}
dv_x_u_space->SetNumericMetaData(iter->first, numeric_md);
}
}
delete[] x_not_fixed_map;
x_not_fixed_map = NULL;
delete[] x_l_map;
x_l_map = NULL;
delete[] x_u_map;
x_u_map = NULL;
// create the required c_space
SmartPtr<DenseVectorSpace> dc_space;
if( n_x_fixed_ == 0 || fixed_variable_treatment_ == MAKE_PARAMETER || fixed_variable_treatment_ == MAKE_PARAMETER_NODUAL )
{
dc_space = new DenseVectorSpace(n_c);
}
else
{
dc_space = new DenseVectorSpace(n_c + n_x_fixed_);
}
c_space_ = GetRawPtr(dc_space);
c_rhs_ = new Number[dc_space->Dim()];
// create the internal expansion matrix for c to g
P_c_g_space_ = new ExpansionMatrixSpace(n_full_g_, n_c, c_map);
P_c_g_ = P_c_g_space_->MakeNewExpansionMatrix();
delete[] c_map;
c_map = NULL;
// create the required d_space
SmartPtr<DenseVectorSpace> dv_d_space = new DenseVectorSpace(n_d);
d_space_ = GetRawPtr(dv_d_space);
// create the internal expansion matrix for d to g
P_d_g_space_ = new ExpansionMatrixSpace(n_full_g_, n_d, d_map);
P_d_g_ = P_d_g_space_->MakeNewExpansionMatrix();
delete[] d_map;
d_map = NULL;
// create the required d_l space
SmartPtr<DenseVectorSpace> dv_d_l_space = new DenseVectorSpace(n_d_l);
d_l_space_ = GetRawPtr(dv_d_l_space);
// create the required expansion matrix for d_L to d_L_exp
SmartPtr<ExpansionMatrixSpace> P_d_l_space = new ExpansionMatrixSpace(n_d, n_d_l, d_l_map);
pd_l_space_ = GetRawPtr(P_d_l_space);
delete[] d_l_map;
d_l_map = NULL;
// create the required d_u space
SmartPtr<DenseVectorSpace> dv_d_u_space = new DenseVectorSpace(n_d_u);
d_u_space_ = GetRawPtr(dv_d_u_space);
// create the required expansion matrix for d_U to d_U_exp
SmartPtr<ExpansionMatrixSpace> P_d_u_space = new ExpansionMatrixSpace(n_d, n_d_u, d_u_map);
pd_u_space_ = GetRawPtr(P_d_u_space);
delete[] d_u_map;
d_u_map = NULL;
delete[] g_l;
g_l = NULL;
delete[] g_u;
g_u = NULL;
// set the constraint meta data if present
if( con_string_md.size() > 0 )
{
for( StringMetaDataMapType::iterator iter = con_string_md.begin(); iter != con_string_md.end(); ++iter )
{
std::vector<std::string> string_md(n_c);
const Index* pos_idx = P_c_g_space_->ExpandedPosIndices();
for( Index i = 0; i < n_c; i++ )
{
string_md[i] = iter->second[pos_idx[i]];
}
dc_space->SetStringMetaData(iter->first, string_md);
string_md.clear();
string_md.resize(n_d);
pos_idx = P_d_g_space_->ExpandedPosIndices();
for( Index i = 0; i < n_d; i++ )
{
string_md[i] = iter->second[pos_idx[i]];
}
dv_d_space->SetStringMetaData(iter->first, string_md);
string_md.clear();
string_md.resize(n_d_l);
const Index* d_pos_idx = P_d_l_space->ExpandedPosIndices();
for( Index i = 0; i < n_d_l; i++ )
{
string_md[i] = iter->second[pos_idx[d_pos_idx[i]]];
}
dv_d_l_space->SetStringMetaData(iter->first, string_md);
string_md.clear();
string_md.resize(n_d_u);
d_pos_idx = P_d_u_space->ExpandedPosIndices();
for( Index i = 0; i < n_d_u; i++ )
{
string_md[i] = iter->second[pos_idx[d_pos_idx[i]]];
}