bool CGPenaltyLSAcceptor::IsAcceptableToPiecewisePenalty(Number alpha_primal_test)
{
DBG_START_METH("CGPenaltyLSAcceptor::IsAcceptableToPiecewisePenalty",
dbg_verbosity);
// If the current infeasibility is small, we require the barrier to be decreased.
bool accept = false;
Number infeasibility = IpCq().curr_primal_infeasibility(NORM_MAX);
SmartPtr<const Vector> dx = IpData().delta()->x();
SmartPtr<const Vector> ds = IpData().delta()->s();
Number curr_barr = IpCq().curr_barrier_obj();
Number trial_barr = IpCq().trial_barrier_obj();
Number nrm_dx_ds = pow(dx->Nrm2(),2.) + pow(ds->Nrm2(),2.);
if (infeasibility < theta_min_) {
Number biggest_barr = PiecewisePenalty_.BiggestBarr();
accept = Compare_le(trial_barr-biggest_barr, -alpha_primal_test*
piecewisepenalty_gamma_obj_*nrm_dx_ds, curr_barr);
if (!accept) {
return false;
}
}
Number Fzconst, Fzlin;
Fzconst = IpCq().trial_barrier_obj() + alpha_primal_test *
piecewisepenalty_gamma_obj_ * nrm_dx_ds;
Fzlin = IpCq().trial_constraint_violation() + alpha_primal_test *
piecewisepenalty_gamma_infeasi_ * IpCq().curr_constraint_violation();
accept = PiecewisePenalty_.Acceptable(Fzconst, Fzlin);
return accept;
}
Number InexactLSAcceptor::ComputeAlphaForY(
Number alpha_primal,
Number alpha_dual,
SmartPtr<IteratesVector>& delta
)
{
DBG_START_METH("InexactLSAcceptor::ComputeAlphaForY",
dbg_verbosity);
// Here, we choose as stepsize for y either alpha_primal, if the
// conditions from the ineqaxt paper is satisfied for it, or we
// compute the step size closest to alpha_primal but great than
// it, that does give the same progress as the full step would
// give.
Number alpha_y = alpha_primal;
SmartPtr<Vector> gx = IpCq().curr_grad_barrier_obj_x()->MakeNewCopy();
gx->AddTwoVectors(1., *IpCq().curr_jac_cT_times_curr_y_c(), 1., *IpCq().curr_jac_dT_times_curr_y_d(), 1.);
SmartPtr<Vector> Jxy = gx->MakeNew();
IpCq().curr_jac_c()->TransMultVector(1., *delta->y_c(), 0., *Jxy);
IpCq().curr_jac_d()->TransMultVector(1., *delta->y_d(), 1., *Jxy);
SmartPtr<const Vector> curr_scaling_slacks = InexCq().curr_scaling_slacks();
SmartPtr<Vector> gs = curr_scaling_slacks->MakeNew();
gs->AddTwoVectors(1., *IpCq().curr_grad_barrier_obj_s(), -1., *IpData().curr()->y_d(), 0.);
gs->ElementWiseMultiply(*curr_scaling_slacks);
SmartPtr<Vector> Sdy = delta->y_d()->MakeNewCopy();
Sdy->ElementWiseMultiply(*curr_scaling_slacks);
// using the magic formula in my notebook
Number a = pow(Jxy->Nrm2(), 2) + pow(Sdy->Nrm2(), 2);
Number b = 2 * (gx->Dot(*Jxy) - gs->Dot(*Sdy));
Number c = pow(gx->Nrm2(), 2) + pow(gs->Nrm2(), 2);
// First we check if the primal step size is good enough:
Number val_ap = alpha_primal * alpha_primal * a + alpha_primal * b + c;
Number val_1 = a + b + c;
if( val_ap <= val_1 )
{
Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, " Step size for y: using alpha_primal\n.");
}
else
{
Number alpha_2 = -b / a - 1.;
Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, " Step size for y candidate: %8.2e - ", alpha_2);
if( alpha_2 > alpha_primal && alpha_2 < 1. )
{
alpha_y = alpha_2;
Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, "using that one\n.");
}
else
{
alpha_y = 1.;
Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, "using 1 instead\n");
}
}
return alpha_y;
}
