bool CGPenaltyLSAcceptor::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
options.GetBoolValue("never_use_piecewise_penalty_ls",
never_use_piecewise_penalty_ls_, prefix);
options.GetNumericValue("eta_penalty", eta_penalty_, prefix);
options.GetNumericValue("penalty_update_infeasibility_tol",
penalty_update_infeasibility_tol_, prefix);
options.GetNumericValue("eta_min", eta_min_, prefix);
options.GetNumericValue("penalty_update_compl_tol",
penalty_update_compl_tol_, prefix);
options.GetNumericValue("chi_hat", chi_hat_, prefix);
options.GetNumericValue("chi_tilde", chi_tilde_, prefix);
options.GetNumericValue("chi_cup", chi_cup_, prefix);
options.GetNumericValue("gamma_hat", gamma_hat_, prefix);
options.GetNumericValue("gamma_tilde", gamma_tilde_, prefix);
options.GetNumericValue("epsilon_c", epsilon_c_, prefix);
options.GetNumericValue("piecewisepenalty_gamma_obj",
piecewisepenalty_gamma_obj_, prefix);
options.GetNumericValue("piecewisepenalty_gamma_infeasi",
piecewisepenalty_gamma_infeasi_, prefix);
options.GetNumericValue("pen_theta_max_fact", pen_theta_max_fact_, prefix);
options.GetNumericValue("min_alpha_primal", min_alpha_primal_, prefix);
options.GetNumericValue("theta_min", theta_min_, prefix);
options.GetNumericValue("mult_diverg_feasibility_tol", mult_diverg_feasibility_tol_, prefix);
options.GetNumericValue("mult_diverg_y_tol", mult_diverg_y_tol_, prefix);
// The following option has been registered by FilterLSAcceptor
options.GetIntegerValue("max_soc", max_soc_, prefix);
// The following option has been registered by CGSearhDirCalc
options.GetNumericValue("penalty_max", penalty_max_, prefix);
if (max_soc_>0) {
ASSERT_EXCEPTION(IsValid(pd_solver_), OPTION_INVALID,
"Option \"max_soc\": This option is non-negative, but no linear solver for computing the SOC given to FilterLSAcceptor object.");
}
options.GetNumericValue("kappa_soc", kappa_soc_, prefix);
pen_theta_max_ = -1.;
pen_curr_mu_ = IpData().curr_mu();
counter_first_type_penalty_updates_ = 0;
counter_second_type_penalty_updates_ = 0;
curr_eta_ = -1.;
CGPenData().SetPenaltyUninitialized();
ls_counter_ = 0;
best_KKT_error_ = -1.;
accepted_by_Armijo_ = true;
//never_do_restor_ = true;
jump_for_tiny_step_ = 0;
return true;
}
bool WsmpSolverInterface::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
options.GetIntegerValue("wsmp_num_threads", wsmp_num_threads_, prefix);
Index wsmp_ordering_option;
options.GetIntegerValue("wsmp_ordering_option", wsmp_ordering_option,
prefix);
Index wsmp_ordering_option2;
options.GetIntegerValue("wsmp_ordering_option2", wsmp_ordering_option2,
prefix);
options.GetNumericValue("wsmp_pivtol", wsmp_pivtol_, prefix);
if (options.GetNumericValue("wsmp_pivtolmax", wsmp_pivtolmax_, prefix)) {
ASSERT_EXCEPTION(wsmp_pivtolmax_>=wsmp_pivtol_, OPTION_INVALID,
"Option \"wsmp_pivtolmax\": This value must be between "
"wsmp_pivtol and 1.");
}
else {
wsmp_pivtolmax_ = Max(wsmp_pivtolmax_, wsmp_pivtol_);
}
options.GetNumericValue("wsmp_singularity_threshold",
wsmp_singularity_threshold_, prefix);
options.GetIntegerValue("wsmp_scaling", wsmp_scaling_, prefix);
options.GetIntegerValue("wsmp_write_matrix_iteration",
wsmp_write_matrix_iteration_, prefix);
options.GetBoolValue("wsmp_skip_inertia_check",
skip_inertia_check_, prefix);
options.GetBoolValue("wsmp_no_pivoting",
wsmp_no_pivoting_, prefix);
// Reset all private data
dim_=0;
initialized_=false;
printed_num_threads_ = false;
pivtol_changed_ = false;
have_symbolic_factorization_ = false;
factorizations_since_recomputed_ordering_ = -1;
delete[] a_;
a_ = NULL;
delete[] PERM_;
PERM_ = NULL;
delete[] INVP_;
INVP_ = NULL;
delete[] MRP_;
MRP_ = NULL;
#ifdef PARDISO_MATCHING_PREPROCESS
delete[] ia2;
ia2 = NULL;
delete[] ja2;
ja2 = NULL;
delete[] a2_;
a2_ = NULL;
delete[] perm2;
perm2 = NULL;
delete[] scale2;
scale2 = NULL;
#endif
// Set the number of threads
ipfint NTHREADS = wsmp_num_threads_;
F77_FUNC(wsetmaxthrds,WSETMAXTHRDS)(&NTHREADS);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"WSMP will use %d threads.\n", wsmp_num_threads_);
// Get WSMP's default parameters and set the ones we want differently
IPARM_[0] = 0;
IPARM_[1] = 0;
IPARM_[2] = 0;
ipfint idmy;
double ddmy;
F77_FUNC(wssmp,WSSMP)(&idmy, &idmy, &idmy, &ddmy, &ddmy, &idmy,
&idmy, &ddmy, &idmy, &idmy, &ddmy, &idmy,
&idmy, IPARM_, DPARM_);
IPARM_[15] = wsmp_ordering_option; // ordering option
IPARM_[17] = 0; // use local minimum fill-in ordering
IPARM_[19] = wsmp_ordering_option2; // for ordering in IP methods?
if (wsmp_no_pivoting_) {
IPARM_[30] = 1; // want L D L^T factorization with diagonal no pivoting
IPARM_[26] = 1;
}
else {
IPARM_[30] = 2; // want L D L^T factorization with diagonal with pivoting
}
// pivoting (Bunch/Kaufman)
//IPARM_[31] = 1; // need D to see where first negative eigenvalue occurs
// if we change this, we need DIAG arguments below!
IPARM_[10] = 2; // Mark bad pivots
// Set WSMP's scaling option
IPARM_[9] = wsmp_scaling_;
DPARM_[9] = wsmp_singularity_threshold_;
matrix_file_number_ = 0;
// Check for SPINLOOPTIME and YIELDLOOPTIME?
return true;
}
bool Ma57TSolverInterface::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
// Obtain the options settings
options.GetNumericValue("ma57_pivtol", pivtol_, prefix);
if (options.GetNumericValue("ma57_pivtolmax", pivtolmax_, prefix)) {
ASSERT_EXCEPTION(pivtolmax_>=pivtol_, OPTION_INVALID,
"Option \"pivtolmax\": This value must be between "
"pivtol and 1.");
}
else {
pivtolmax_ = Max(pivtolmax_, pivtol_);
}
options.GetNumericValue("ma57_pre_alloc", ma57_pre_alloc_, prefix);
Index ma57_pivot_order;
options.GetIntegerValue("ma57_pivot_order", ma57_pivot_order, prefix);
// The following option is registered by OrigIpoptNLP
options.GetBoolValue("warm_start_same_structure",
warm_start_same_structure_, prefix);
DBG_ASSERT(!warm_start_same_structure_ && "warm_start_same_structure not yet implemented");
bool ma57_automatic_scaling;
options.GetBoolValue("ma57_automatic_scaling", ma57_automatic_scaling, prefix);
// CET 04-29-2010
Index ma57_block_size;
options.GetIntegerValue("ma57_block_size", ma57_block_size, prefix);
Index ma57_node_amalgamation;
options.GetIntegerValue("ma57_node_amalgamation", ma57_node_amalgamation, prefix);
Index ma57_small_pivot_flag;
options.GetIntegerValue("ma57_small_pivot_flag", ma57_small_pivot_flag, prefix);
// CET 04-29-2010
/* Initialize. */
F77_FUNC (ma57id, MA57ID) (wd_cntl_, wd_icntl_);
/* Custom settings for MA57. */
wd_icntl_[1-1] = 0; /* Error stream */
wd_icntl_[2-1] = 0; /* Warning stream. */
wd_icntl_[4-1] = 1; /* Print statistics. NOT Used. */
wd_icntl_[5-1] = 0; /* Print error. */
wd_icntl_[6-1] = ma57_pivot_order; /* Pivoting order. */
wd_cntl_[1-1] = pivtol_; /* Pivot threshold. */
wd_icntl_[7-1] = 1; /* Pivoting strategy. */
// CET: Added 04-29-2010 at suggestion of Jonathan Hogg of HSL
wd_icntl_[11-1] = ma57_block_size; /* Block size used by Level 3 BLAS in MA57BD - should be a multiple of 8. Default is 16. */
wd_icntl_[12-1] = ma57_node_amalgamation; /* Two nodes of the assembly tree are merged only if both involve less than ICNTL(12) eliminations. Default is 16. */
// CET: 04-29-2010
if (ma57_automatic_scaling) {
wd_icntl_[15-1] = 1;
}
else {
wd_icntl_[15-1] = 0;
}
// CET: Added 04-29-2010 at suggestion of Jonathan Hogg of HSL
wd_icntl_[16-1] = ma57_small_pivot_flag; /* If set to 1, small entries are removed and corresponding pivots are placed at the end of factorization. May be useful for highly rank deficient matrices. Default is 0. */
// CET: 04-29-2010
// wd_icntl[8-1] = 0; /* Retry factorization. */
if (!warm_start_same_structure_) {
dim_=0;
nonzeros_=0;
delete [] a_;
a_ = NULL;
delete [] wd_fact_;
wd_fact_ = NULL;
delete [] wd_ifact_;
wd_ifact_ = NULL;
delete [] wd_iwork_;
wd_iwork_ = NULL;
delete [] wd_keep_;
wd_keep_ = NULL;
}
else {
ASSERT_EXCEPTION(dim_>0 && nonzeros_>0, INVALID_WARMSTART,
"Ma57TSolverInterface called with warm_start_same_structure, "
"but the problem is solved for the first time.");
}
return true;
}
bool WarmStartIterateInitializer::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
if (!options.GetNumericValue("warm_start_bound_push",
warm_start_bound_push_, prefix)) {
options.GetNumericValue("bound_push",
warm_start_bound_push_, prefix);
}
if (!options.GetNumericValue("warm_start_bound_frac",
warm_start_bound_frac_, prefix)) {
options.GetNumericValue("bound_frac",
warm_start_bound_frac_, prefix);
}
if (!options.GetNumericValue("warm_start_slack_bound_push",
warm_start_slack_bound_push_, prefix)) {
if (!options.GetNumericValue("bound_push",
warm_start_slack_bound_push_, prefix)) {
if (!options.GetNumericValue("warm_start_slack_bound_push",
warm_start_slack_bound_push_, prefix)) {
options.GetNumericValue("bound_push",
warm_start_slack_bound_push_, prefix);
}
}
}
if (!options.GetNumericValue("warm_start_slack_bound_frac",
warm_start_slack_bound_frac_, prefix)) {
if (!options.GetNumericValue("bound_frac",
warm_start_slack_bound_frac_, prefix)) {
if (!options.GetNumericValue("warm_start_slack_bound_frac",
warm_start_slack_bound_frac_, prefix)) {
options.GetNumericValue("bound_frac",
warm_start_slack_bound_frac_, prefix);
}
}
}
options.GetNumericValue("warm_start_mult_bound_push",
warm_start_mult_bound_push_, prefix);
options.GetNumericValue("warm_start_mult_init_max",
warm_start_mult_init_max_, prefix);
options.GetNumericValue("warm_start_target_mu",
warm_start_target_mu_, prefix);
options.GetBoolValue("warm_start_entire_iterate",
warm_start_entire_iterate_, prefix);
return true;
}
bool PardisoSolverInterface::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
Index enum_int;
options.GetEnumValue("pardiso_matching_strategy", enum_int, prefix);
match_strat_ = PardisoMatchingStrategy(enum_int);
options.GetBoolValue("pardiso_redo_symbolic_fact_only_if_inertia_wrong",
pardiso_redo_symbolic_fact_only_if_inertia_wrong_,
prefix);
options.GetBoolValue("pardiso_repeated_perturbation_means_singular",
pardiso_repeated_perturbation_means_singular_,
prefix);
//Index pardiso_out_of_core_power;
//options.GetIntegerValue("pardiso_out_of_core_power",
// pardiso_out_of_core_power, prefix);
options.GetBoolValue("pardiso_skip_inertia_check",
skip_inertia_check_, prefix);
int pardiso_msglvl;
options.GetIntegerValue("pardiso_msglvl", pardiso_msglvl, prefix);
int max_iterref_steps;
options.GetIntegerValue("pardiso_max_iterative_refinement_steps", max_iterref_steps, prefix);
int order;
options.GetEnumValue("pardiso_order", order, prefix);
#if !defined(HAVE_PARDISO_OLDINTERFACE) && !defined(HAVE_PARDISO_MKL)
options.GetBoolValue("pardiso_iterative", pardiso_iterative_, prefix);
int pardiso_max_iter;
options.GetIntegerValue("pardiso_max_iter", pardiso_max_iter, prefix);
Number pardiso_iter_relative_tol;
options.GetNumericValue("pardiso_iter_relative_tol",
pardiso_iter_relative_tol, prefix);
Index pardiso_iter_coarse_size;
options.GetIntegerValue("pardiso_iter_coarse_size",
pardiso_iter_coarse_size, prefix);
Index pardiso_iter_max_levels;
options.GetIntegerValue("pardiso_iter_max_levels",
pardiso_iter_max_levels, prefix);
Number pardiso_iter_dropping_factor;
options.GetNumericValue("pardiso_iter_dropping_factor",
pardiso_iter_dropping_factor, prefix);
Number pardiso_iter_dropping_schur;
options.GetNumericValue("pardiso_iter_dropping_schur",
pardiso_iter_dropping_schur, prefix);
Index pardiso_iter_max_row_fill;
options.GetIntegerValue("pardiso_iter_max_row_fill",
pardiso_iter_max_row_fill, prefix);
Number pardiso_iter_inverse_norm_factor;
options.GetNumericValue("pardiso_iter_inverse_norm_factor",
pardiso_iter_inverse_norm_factor, prefix);
options.GetIntegerValue("pardiso_max_droptol_corrections",
pardiso_max_droptol_corrections_, prefix);
#else
pardiso_iterative_ = false;
#endif
// Number value = 0.0;
// Tell Pardiso to release all memory if it had been used before
if (initialized_) {
ipfint PHASE = -1;
ipfint N = dim_;
ipfint NRHS = 0;
ipfint ERROR;
ipfint idmy;
double ddmy;
PARDISO_FUNC(PT_, &MAXFCT_, &MNUM_, &MTYPE_, &PHASE, &N,
&ddmy, &idmy, &idmy, &idmy, &NRHS, IPARM_,
&MSGLVL_, &ddmy, &ddmy, &ERROR, DPARM_);
DBG_ASSERT(ERROR==0);
}
// Reset all private data
dim_=0;
nonzeros_=0;
have_symbolic_factorization_=false;
initialized_=false;
delete[] a_;
a_ = NULL;
#ifdef PARDISO_MATCHING_PREPROCESS
delete[] ia2;
ia2 = NULL;
delete[] ja2;
ja2 = NULL;
delete[] a2_;
a2_ = NULL;
delete[] perm2;
perm2 = NULL;
delete[] scale2;
scale2 = NULL;
#endif
// Call Pardiso's initialization routine
IPARM_[0] = 0; // Tell it to fill IPARM with default values(?)
#if ! defined(HAVE_PARDISO_OLDINTERFACE) && ! defined(HAVE_PARDISO_MKL)
ipfint ERROR = 0;
ipfint SOLVER = 0; // initialize only direct solver
PARDISOINIT_FUNC(PT_, &MTYPE_, &SOLVER, IPARM_, DPARM_, &ERROR);
#else
PARDISOINIT_FUNC(PT_, &MTYPE_, IPARM_);
#endif
// Set some parameters for Pardiso
IPARM_[0] = 1; // Don't use the default values
int num_procs = 1;
#if defined(HAVE_PARDISO_PARALLEL) || ! defined(HAVE_PARDISO)
// Obtain the numbers of processors from the value of OMP_NUM_THREADS
char* var = getenv("OMP_NUM_THREADS");
if (var != NULL) {
sscanf( var, "%d", &num_procs );
if (num_procs < 1) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Invalid value for OMP_NUM_THREADS (\"%s\").\n", var);
return false;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Using environment OMP_NUM_THREADS = %d as the number of processors for PARDISO.\n", num_procs);
}
#if defined(HAVE_PARDISO) && ! defined(HAVE_PARDISO_MKL)
// If we run Pardiso through the linear solver loader,
// we do not know whether it is the parallel version, so we do not report a warning if OMP_NUM_THREADS is not set.
// If we run Pardiso from MKL, then OMP_NUM_THREADS does not need to be set, so no warning.
else {
Jnlst().Printf(J_WARNING, J_LINEAR_ALGEBRA,
"You should set the environment variable OMP_NUM_THREADS to the number of processors used in Pardiso (e.g., 1).\n\n");
}
#endif
#endif
#ifdef HAVE_PARDISO_MKL
IPARM_[1] = order;
// For MKL PARDSIO, the documentation says, "iparm(3) Reserved. Set to zero.", so we don't set IPARM_[2]
IPARM_[5] = 1; // Overwrite right-hand side
IPARM_[7] = max_iterref_steps;
IPARM_[9] = 12; // pivot perturbation (as higher as less perturbation)
IPARM_[10] = 2; // enable scaling (recommended for interior-point indefinite matrices)
IPARM_[12] = (int)match_strat_; // enable matching (recommended, as above)
IPARM_[20] = 3; // bunch-kaufman pivoting
IPARM_[23] = 1; // parallel fac
IPARM_[24] = 1; // parallel solve
//IPARM_[26] = 1; // matrix checker
#else
IPARM_[1] = order;
IPARM_[2] = num_procs; // Set the number of processors
IPARM_[5] = 1; // Overwrite right-hand side
IPARM_[7] = max_iterref_steps;
// Options suggested by Olaf Schenk
IPARM_[9] = 12;
IPARM_[10] = 2; // Results in better scaling
// Matching information: IPARM_[12] = 1 seems ok, but results in a
// large number of pivot perturbation
// Matching information: IPARM_[12] = 2 robust, but more expensive method
IPARM_[12] = (int)match_strat_;
IPARM_[20] = 3; // Results in better accuracy
IPARM_[23] = 1; // parallel fac
IPARM_[24] = 1; // parallel solve
IPARM_[28] = 0; // 32-bit factorization
IPARM_[29] = 1; //we need this for IPOPT interface
//IPARM_[33] = 1; // bit-by-bit identical results in parallel run
#endif
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Pardiso matrix ordering (IPARM(2)): %d\n", IPARM_[1]);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Pardiso max. iterref. steps (IPARM(8)): %d\n", IPARM_[7]);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Pardiso matching strategy (IPARM(13)): %d\n", IPARM_[12]);
if (pardiso_iterative_) {
#if defined(HAVE_PARDISO_OLDINTERFACE) || defined(HAVE_PARDISO_MKL)
THROW_EXCEPTION(OPTION_INVALID,
"You chose to use the iterative version of Pardiso, but you need to use a Pardiso version of at least 4.0.");
#else
IPARM_[31] = 1 ; // active direct solver
DPARM_[ 0] = pardiso_max_iter; // maximum number of Krylov-Subspace Iteration
// Default is 300
// 1 <= value <= e.g. 1000
DPARM_[ 1] = pardiso_iter_relative_tol; // Relative Residual Convergence
// e.g. pardiso_iter_tol
// Default is 1e-6
// 1e-16 <= value < 1
DPARM_[ 2] = pardiso_iter_coarse_size; // Maximum Size of Coarse Grid Matrix
// e.g. pardiso_coarse_grid
// Default is 5000
// 1 <= value < number of equations
DPARM_[ 3] = pardiso_iter_max_levels; // Maximum Number of Grid Levels
// e.g. pardiso_max_grid
// Default is 10000
// 1 <= value < number of equations
DPARM_[ 4] = pardiso_iter_dropping_factor; // dropping value for incomplete factor
// e.g. pardiso_dropping_factor
// Default is 0.5
// 1e-16 <= value < 1
DPARM_[ 5] = pardiso_iter_dropping_schur; // dropping value for sparsify schur complementfactor
// e.g. pardiso_dropping_schur
// Default is 0.1
// 1e-16 <= value < 1
DPARM_[ 6] = pardiso_iter_max_row_fill; // max fill for each row
// e.g. pardiso_max_fill
// Default is 1000
// 1 <= value < 100000
DPARM_[ 7] = pardiso_iter_inverse_norm_factor; // dropping value for sparsify schur complementfactor
// e.g. pardiso_inverse_norm_factor
// Default is 500
// 2 <= value < 50000
DPARM_[ 8] = 25; // maximum number of non-improvement steps
#endif
}
MSGLVL_ = pardiso_msglvl;
// Option for the out of core variant
//IPARM_[49] = pardiso_out_of_core_power;
return true;
}
bool StandardScalingBase::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
options.GetNumericValue("obj_scaling_factor", obj_scaling_factor_, prefix);
return true;
}
bool GradientScaling::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
options.GetNumericValue("nlp_scaling_max_gradient", scaling_max_gradient_, prefix);
return StandardScalingBase::InitializeImpl(options, prefix);
}
bool WsmpSolverInterface::InitializeImpl(const OptionsList& options,
const std::string& prefix)
{
options.GetIntegerValue("wsmp_num_threads", wsmp_num_threads_, prefix);
options.GetIntegerValue("wsmp_ordering_option", wsmp_ordering_option_,
prefix);
options.GetNumericValue("wsmp_pivtol", wsmp_pivtol_, prefix);
if(options.GetNumericValue("wsmp_pivtolmax", wsmp_pivtolmax_, prefix)) {
ASSERT_EXCEPTION(wsmp_pivtolmax_>=wsmp_pivtol_, OPTION_INVALID,
"Option \"wsmp_pivtolmax\": This value must be between "
"wsmp_pivtol and 1.");
}
else {
wsmp_pivtolmax_ = Max(wsmp_pivtolmax_, wsmp_pivtol_);
}
options.GetNumericValue("wsmp_singularity_threshold",
wsmp_singularity_threshold_, prefix);
options.GetIntegerValue("wsmp_scaling", wsmp_scaling_, prefix);
options.GetIntegerValue("wsmp_write_matrix_iteration",
wsmp_write_matrix_iteration_, prefix);
// Reset all private data
dim_=0;
initialized_=false;
pivtol_changed_ = false;
have_symbolic_factorization_ = false;
delete[] a_;
delete[] PERM_;
delete[] INVP_;
// Set the number of threads
ipfint NTHREADS = wsmp_num_threads_;
F77_FUNC(wsetmaxthrds,WSETMAXTHRDS)(&NTHREADS);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"WSMP will use %d threads.\n", wsmp_num_threads_);
// Get WSMP's default parameters and set the ones we want differently
IPARM_[0] = 0;
IPARM_[1] = 0;
IPARM_[2] = 0;
ipfint idmy;
double ddmy;
F77_FUNC(wssmp,WSSMP)(&idmy, &idmy, &idmy, &ddmy, &ddmy, &idmy,
&idmy, &ddmy, &idmy, &idmy, &ddmy, &idmy,
&idmy, IPARM_, DPARM_);
IPARM_[14] = 0; // no restrictions on pivoting (ignored for
// Bunch-Kaufman)
IPARM_[15] = wsmp_ordering_option_; // ordering option
IPARM_[17] = 1; // use local minimum fill-in ordering
IPARM_[19] = 1; // for ordering in IP methods?
IPARM_[30] = 2; // want L D L^T factorization with diagonal
// pivoting (Bunch/Kaufman)
//IPARM_[31] = 1; // need D to see where first negative eigenvalue occurs
// if we change this, we need DIAG arguments below!
IPARM_[10] = 1; // THis is not used by Bunch/Kaufman, but for L D
// L^T without pivoting it is the value we used
// before
// Set WSMP's scaling option
IPARM_[9] = wsmp_scaling_;
DPARM_[9] = wsmp_singularity_threshold_;
matrix_file_number_ = 0;
// Check for SPINLOOPTIME and YIELDLOOPTIME?
return true;
}
