void IpoptAlgorithm::calc_number_of_bounds(
const Vector& x,
const Vector& x_L,
const Vector& x_U,
const Matrix& Px_L,
const Matrix& Px_U,
Index& n_tot,
Index& n_only_lower,
Index& n_both,
Index& n_only_upper)
{
DBG_START_METH("IpoptAlgorithm::calc_number_of_bounds",
dbg_verbosity);
n_tot = x.Dim();
SmartPtr<Vector> tmpx = x.MakeNew();
SmartPtr<Vector> tmpxL = x_L.MakeNew();
SmartPtr<Vector> tmpxU = x_U.MakeNew();
tmpxL->Set(-1.);
tmpxU->Set(2.);
Px_L.MultVector(1.0, *tmpxL, 0.0, *tmpx);
Px_U.MultVector(1.0, *tmpxU, 1.0, *tmpx);
// Now, x has elements
// -1 : if component has only lower bound
// 0 : if component has no bound
// 1 : if component has both lower and upper bound
// 2 : if component has only upper bound
DBG_PRINT_VECTOR(2, "x-indicator", *tmpx);
SmartPtr<Vector> tmpx0 = x.MakeNew();
tmpx0->Set(0.);
SmartPtr<Vector> tmpx2 = x.MakeNew();
tmpx2->Set(-1.0);
tmpx2->Axpy(1.0, *tmpx);
tmpx2->ElementWiseMax(*tmpx0); // tmpx2 is now 1 in those
// components with only upper bounds
n_only_upper = (Index)tmpx2->Asum();
tmpx->Axpy(-2., *tmpx2); // now make all those entries for
// only upper bounds zero in tmpx
tmpx2->Copy(*tmpx);
tmpx2->ElementWiseMax(*tmpx0); // tmpx2 is now 1 in those
// components with both bounds
n_both = (Index)tmpx2->Asum();
tmpx->Axpy(-1., *tmpx2);
tmpx->ElementWiseMin(*tmpx); // tmpx is now -1 in those with only
// lower bounds
n_only_lower = (Index)tmpx->Asum();
}
SmartPtr<const Vector>
AugRestoSystemSolver::Neg_Omega_c_plus_D_c(
const SmartPtr<const Vector>& sigma_tilde_n_c_inv,
const SmartPtr<const Vector>& sigma_tilde_p_c_inv,
const Vector* D_c,
const Vector& any_vec_in_c)
{
DBG_START_METH("AugRestoSystemSolver::Neg_Omega_c_plus_D_c",dbg_verbosity);
SmartPtr<Vector> retVec;
if (IsValid(sigma_tilde_n_c_inv) || IsValid(sigma_tilde_p_c_inv) || D_c) {
if (!neg_omega_c_plus_D_c_cache_.
GetCachedResult3Dep(retVec, GetRawPtr(sigma_tilde_n_c_inv), GetRawPtr(sigma_tilde_p_c_inv), D_c)) {
DBG_PRINT((1,"Not found in cache\n"));
retVec = any_vec_in_c.MakeNew();
Number fact1, fact2;
SmartPtr<const Vector> v1;
SmartPtr<const Vector> v2;
if (IsValid(sigma_tilde_n_c_inv)) {
v1 = sigma_tilde_n_c_inv;
fact1 = -1.;
}
else {
v1 = &any_vec_in_c;
fact1 = 0.;
}
if (IsValid(sigma_tilde_p_c_inv)) {
v2 = sigma_tilde_p_c_inv;
fact2 = -1.;
}
else {
v2 = &any_vec_in_c;
fact2 = 0.;
}
retVec->AddTwoVectors(fact1, *v1, fact2, *v2, 0.);
if (D_c) {
retVec->Axpy(1.0, *D_c);
}
neg_omega_c_plus_D_c_cache_.
AddCachedResult3Dep(retVec, GetRawPtr(sigma_tilde_n_c_inv), GetRawPtr(sigma_tilde_p_c_inv), D_c);
}
}
return ConstPtr(retVec);
}
bool
RestoRestorationPhase::PerformRestoration()
{
DBG_START_METH("RestoRestorationPhase::PerformRestoration",
dbg_verbosity);
Jnlst().Printf(J_DETAILED, J_MAIN,
"Performing second level restoration phase for current constriant violation %8.2e\n", IpCq().curr_constraint_violation());
DBG_ASSERT(IpCq().curr_constraint_violation()>0.);
// Get a grip on the restoration phase NLP and obtain the pointers
// to the original NLP data
SmartPtr<RestoIpoptNLP> resto_ip_nlp =
static_cast<RestoIpoptNLP*> (&IpNLP());
DBG_ASSERT(dynamic_cast<RestoIpoptNLP*> (&IpNLP()));
SmartPtr<IpoptNLP> orig_ip_nlp =
static_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP());
DBG_ASSERT(dynamic_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP()));
// Get the current point and create a new vector for the result
SmartPtr<const CompoundVector> Ccurr_x =
static_cast<const CompoundVector*> (GetRawPtr(IpData().curr()->x()));
SmartPtr<Vector> new_x = IpData().curr()->x()->MakeNew();
SmartPtr<CompoundVector> Cnew_x =
static_cast<CompoundVector*> (GetRawPtr(new_x));
// The x values remain unchanged
SmartPtr<Vector> x = Cnew_x->GetCompNonConst(0);
x->Copy(*Ccurr_x->GetComp(0));
// ToDo in free mu mode - what to do here?
Number mu = IpData().curr_mu();
// Compute the initial values for the n and p variables for the
// equality constraints
Number rho = resto_ip_nlp->Rho();
SmartPtr<Vector> nc = Cnew_x->GetCompNonConst(1);
SmartPtr<Vector> pc = Cnew_x->GetCompNonConst(2);
SmartPtr<const Vector> cvec = orig_ip_nlp->c(*Ccurr_x->GetComp(0));
SmartPtr<Vector> a = nc->MakeNew();
SmartPtr<Vector> b = nc->MakeNew();
a->Set(mu/(2.*rho));
a->Axpy(-0.5, *cvec);
b->Copy(*cvec);
b->Scal(mu/(2.*rho));
solve_quadratic(*a, *b, *nc);
pc->Copy(*cvec);
pc->Axpy(1., *nc);
DBG_PRINT_VECTOR(2, "nc", *nc);
DBG_PRINT_VECTOR(2, "pc", *pc);
// initial values for the n and p variables for the inequality
// constraints
SmartPtr<Vector> nd = Cnew_x->GetCompNonConst(3);
SmartPtr<Vector> pd = Cnew_x->GetCompNonConst(4);
SmartPtr<Vector> dvec = pd->MakeNew();
dvec->Copy(*orig_ip_nlp->d(*Ccurr_x->GetComp(0)));
dvec->Axpy(-1., *IpData().curr()->s());
a = nd->MakeNew();
b = nd->MakeNew();
a->Set(mu/(2.*rho));
a->Axpy(-0.5, *dvec);
b->Copy(*dvec);
b->Scal(mu/(2.*rho));
solve_quadratic(*a, *b, *nd);
pd->Copy(*dvec);
pd->Axpy(1., *nd);
DBG_PRINT_VECTOR(2, "nd", *nd);
DBG_PRINT_VECTOR(2, "pd", *pd);
// Now set the trial point to the solution of the restoration phase
// s and all multipliers remain unchanged
SmartPtr<IteratesVector> new_trial = IpData().curr()->MakeNewContainer();
new_trial->Set_x(*new_x);
IpData().set_trial(new_trial);
IpData().Append_info_string("R");
return true;
}
bool RestoIterateInitializer::SetInitialIterates()
{
DBG_START_METH("RestoIterateInitializer::SetInitialIterates",
dbg_verbosity);
// Get a grip on the restoration phase NLP and obtain the pointers
// to the original NLP data
SmartPtr<RestoIpoptNLP> resto_ip_nlp =
static_cast<RestoIpoptNLP*> (&IpNLP());
SmartPtr<IpoptNLP> orig_ip_nlp =
static_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP());
SmartPtr<IpoptData> orig_ip_data =
static_cast<IpoptData*> (&resto_ip_nlp->OrigIpData());
SmartPtr<IpoptCalculatedQuantities> orig_ip_cq =
static_cast<IpoptCalculatedQuantities*> (&resto_ip_nlp->OrigIpCq());
// Set the value of the barrier parameter
Number resto_mu;
resto_mu = Max(orig_ip_data->curr_mu(),
orig_ip_cq->curr_c()->Amax(),
orig_ip_cq->curr_d_minus_s()->Amax());
IpData().Set_mu(resto_mu);
Jnlst().Printf(J_DETAILED, J_INITIALIZATION,
"Initial barrier parameter resto_mu = %e\n", resto_mu);
/////////////////////////////////////////////////////////////////////
// Initialize primal varialbes //
/////////////////////////////////////////////////////////////////////
// initialize the data structures in the restoration phase NLP
IpData().InitializeDataStructures(IpNLP(), false, false, false,
false, false);
SmartPtr<Vector> new_x = IpData().curr()->x()->MakeNew();
SmartPtr<CompoundVector> Cnew_x =
static_cast<CompoundVector*> (GetRawPtr(new_x));
// Set the trial x variables from the original NLP
Cnew_x->GetCompNonConst(0)->Copy(*orig_ip_data->curr()->x());
// Compute the initial values for the n and p variables for the
// equality constraints
Number rho = resto_ip_nlp->Rho();
DBG_PRINT((1,"rho = %e\n", rho));
SmartPtr<Vector> nc = Cnew_x->GetCompNonConst(1);
SmartPtr<Vector> pc = Cnew_x->GetCompNonConst(2);
SmartPtr<const Vector> cvec = orig_ip_cq->curr_c();
DBG_PRINT_VECTOR(2, "cvec", *cvec);
SmartPtr<Vector> a = nc->MakeNew();
SmartPtr<Vector> b = nc->MakeNew();
a->Set(resto_mu/(2.*rho));
a->Axpy(-0.5, *cvec);
b->Copy(*cvec);
b->Scal(resto_mu/(2.*rho));
DBG_PRINT_VECTOR(2, "a", *a);
DBG_PRINT_VECTOR(2, "b", *b);
solve_quadratic(*a, *b, *nc);
pc->Copy(*cvec);
pc->Axpy(1., *nc);
DBG_PRINT_VECTOR(2, "nc", *nc);
DBG_PRINT_VECTOR(2, "pc", *pc);
// initial values for the n and p variables for the inequality
// constraints
SmartPtr<Vector> nd = Cnew_x->GetCompNonConst(3);
SmartPtr<Vector> pd = Cnew_x->GetCompNonConst(4);
cvec = orig_ip_cq->curr_d_minus_s();
a = nd->MakeNew();
b = nd->MakeNew();
a->Set(resto_mu/(2.*rho));
a->Axpy(-0.5, *cvec);
b->Copy(*cvec);
b->Scal(resto_mu/(2.*rho));
solve_quadratic(*a, *b, *nd);
pd->Copy(*cvec);
pd->Axpy(1., *nd);
DBG_PRINT_VECTOR(2, "nd", *nd);
DBG_PRINT_VECTOR(2, "pd", *pd);
// Leave the slacks unchanged
SmartPtr<const Vector> new_s = orig_ip_data->curr()->s();
// Now set the primal trial variables
DBG_PRINT_VECTOR(2,"new_s",*new_s);
DBG_PRINT_VECTOR(2,"new_x",*new_x);
SmartPtr<IteratesVector> trial = IpData().curr()->MakeNewContainer();
trial->Set_primal(*new_x, *new_s);
IpData().set_trial(trial);
DBG_PRINT_VECTOR(2, "resto_c", *IpCq().trial_c());
DBG_PRINT_VECTOR(2, "resto_d_minus_s", *IpCq().trial_d_minus_s());
/////////////////////////////////////////////////////////////////////
// Initialize bound multipliers //
/////////////////////////////////////////////////////////////////////
SmartPtr<Vector> new_z_L = IpData().curr()->z_L()->MakeNew();
SmartPtr<CompoundVector> Cnew_z_L =
static_cast<CompoundVector*> (GetRawPtr(new_z_L));
DBG_ASSERT(IsValid(Cnew_z_L));
SmartPtr<Vector> new_z_U = IpData().curr()->z_U()->MakeNew();
SmartPtr<Vector> new_v_L = IpData().curr()->v_L()->MakeNew();
SmartPtr<Vector> new_v_U = IpData().curr()->v_U()->MakeNew();
// multipliers for the original bounds are
SmartPtr<const Vector> orig_z_L = orig_ip_data->curr()->z_L();
SmartPtr<const Vector> orig_z_U = orig_ip_data->curr()->z_U();
SmartPtr<const Vector> orig_v_L = orig_ip_data->curr()->v_L();
SmartPtr<const Vector> orig_v_U = orig_ip_data->curr()->v_U();
// Set the new multipliers to the min of the penalty parameter Rho
// and their current value
SmartPtr<Vector> Cnew_z_L0 = Cnew_z_L->GetCompNonConst(0);
Cnew_z_L0->Set(rho);
Cnew_z_L0->ElementWiseMin(*orig_z_L);
new_z_U->Set(rho);
new_z_U->ElementWiseMin(*orig_z_U);
new_v_L->Set(rho);
new_v_L->ElementWiseMin(*orig_v_L);
new_v_U->Set(rho);
new_v_U->ElementWiseMin(*orig_v_U);
// Set the multipliers for the p and n bounds to the "primal" multipliers
SmartPtr<Vector> Cnew_z_L1 = Cnew_z_L->GetCompNonConst(1);
Cnew_z_L1->Set(resto_mu);
Cnew_z_L1->ElementWiseDivide(*nc);
SmartPtr<Vector> Cnew_z_L2 = Cnew_z_L->GetCompNonConst(2);
Cnew_z_L2->Set(resto_mu);
Cnew_z_L2->ElementWiseDivide(*pc);
SmartPtr<Vector> Cnew_z_L3 = Cnew_z_L->GetCompNonConst(3);
Cnew_z_L3->Set(resto_mu);
Cnew_z_L3->ElementWiseDivide(*nd);
SmartPtr<Vector> Cnew_z_L4 = Cnew_z_L->GetCompNonConst(4);
Cnew_z_L4->Set(resto_mu);
Cnew_z_L4->ElementWiseDivide(*pd);
// Set those initial values to be the trial values in Data
trial = IpData().trial()->MakeNewContainer();
trial->Set_bound_mult(*new_z_L, *new_z_U, *new_v_L, *new_v_U);
IpData().set_trial(trial);
/////////////////////////////////////////////////////////////////////
// Initialize equality constraint multipliers //
/////////////////////////////////////////////////////////////////////
DefaultIterateInitializer::least_square_mults(
Jnlst(), IpNLP(), IpData(), IpCq(),
resto_eq_mult_calculator_, constr_mult_init_max_);
// upgrade the trial to the current point
IpData().AcceptTrialPoint();
DBG_PRINT_VECTOR(2, "y_c", *IpData().curr()->y_c());
DBG_PRINT_VECTOR(2, "y_d", *IpData().curr()->y_d());
DBG_PRINT_VECTOR(2, "z_L", *IpData().curr()->z_L());
DBG_PRINT_VECTOR(2, "z_U", *IpData().curr()->z_U());
DBG_PRINT_VECTOR(2, "v_L", *IpData().curr()->v_L());
DBG_PRINT_VECTOR(2, "v_U", *IpData().curr()->v_U());
return true;
}
bool PDSearchDirCalculator::ComputeSearchDirection()
{
DBG_START_METH("PDSearchDirCalculator::ComputeSearchDirection",
dbg_verbosity);
bool improve_solution = false;
if (IpData().HaveDeltas()) {
improve_solution = true;
}
bool retval;
if (improve_solution && fast_step_computation_) {
retval = true;
}
else {
SmartPtr<IteratesVector> rhs = IpData().curr()->MakeNewContainer();
rhs->Set_x(*IpCq().curr_grad_lag_with_damping_x());
rhs->Set_s(*IpCq().curr_grad_lag_with_damping_s());
rhs->Set_y_c(*IpCq().curr_c());
rhs->Set_y_d(*IpCq().curr_d_minus_s());
Index nbounds = IpNLP().x_L()->Dim()+ IpNLP().x_U()->Dim() +
IpNLP().d_L()->Dim()+ IpNLP().d_U()->Dim();
if (nbounds>0 && mehrotra_algorithm_) {
// set up the right hand side a la Mehrotra
DBG_ASSERT(IpData().HaveAffineDeltas());
DBG_ASSERT(!IpData().HaveDeltas());
const SmartPtr<const IteratesVector> delta_aff = IpData().delta_aff();
SmartPtr<Vector> tmpvec = delta_aff->z_L()->MakeNew();
IpNLP().Px_L()->TransMultVector(1., *delta_aff->x(), 0., *tmpvec);
tmpvec->ElementWiseMultiply(*delta_aff->z_L());
tmpvec->Axpy(1., *IpCq().curr_relaxed_compl_x_L());
rhs->Set_z_L(*tmpvec);
tmpvec = delta_aff->z_U()->MakeNew();
IpNLP().Px_U()->TransMultVector(-1., *delta_aff->x(), 0., *tmpvec);
tmpvec->ElementWiseMultiply(*delta_aff->z_U());
tmpvec->Axpy(1., *IpCq().curr_relaxed_compl_x_U());
rhs->Set_z_U(*tmpvec);
tmpvec = delta_aff->v_L()->MakeNew();
IpNLP().Pd_L()->TransMultVector(1., *delta_aff->s(), 0., *tmpvec);
tmpvec->ElementWiseMultiply(*delta_aff->v_L());
tmpvec->Axpy(1., *IpCq().curr_relaxed_compl_s_L());
rhs->Set_v_L(*tmpvec);
tmpvec = delta_aff->v_U()->MakeNew();
IpNLP().Pd_U()->TransMultVector(-1., *delta_aff->s(), 0., *tmpvec);
tmpvec->ElementWiseMultiply(*delta_aff->v_U());
tmpvec->Axpy(1., *IpCq().curr_relaxed_compl_s_U());
rhs->Set_v_U(*tmpvec);
}
else {
rhs->Set_z_L(*IpCq().curr_relaxed_compl_x_L());
rhs->Set_z_U(*IpCq().curr_relaxed_compl_x_U());
rhs->Set_v_L(*IpCq().curr_relaxed_compl_s_L());
rhs->Set_v_U(*IpCq().curr_relaxed_compl_s_U());
}
DBG_PRINT_VECTOR(2, "rhs", *rhs);
// Get space for the search direction
SmartPtr<IteratesVector> delta =
IpData().curr()->MakeNewIteratesVector(true);
if (improve_solution) {
// We can probably avoid copying and scaling...
delta->AddOneVector(-1., *IpData().delta(), 0.);
}
bool& allow_inexact = fast_step_computation_;
retval = pd_solver_->Solve(-1.0, 0.0, *rhs, *delta, allow_inexact,
improve_solution);
if (retval) {
// Store the search directions in the IpData object
IpData().set_delta(delta);
}
}
return retval;
}
