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it is terminating now

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vasiloglou
2008-02-16 06:33:35 +00:00
parent c441f3e714
commit 3dbdd1d114
1 changed files with 6 additions and 6 deletions
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fastlib2/contrib/nvasil/non_convex_mvu/non_convex_mvu_impl.h
+6 -6
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@@ -22,7 +22,7 @@ NonConvexMVU::NonConvexMVU() {
sigma_ = 1e1;
step_size_ = 2;
max_iterations_ = 100000;
tolerance_ =5* 1e-5;
tolerance_ = 1e-4;
armijo_sigma_=1e-1;
armijo_beta_=0.5;
new_dimension_ = -1;
@@ -92,7 +92,6 @@ void NonConvexMVU::ComputeLocalOptimumBFGS() {
double distance_constraint;
double centering_constraint;
double step;
double sum_of_dist_square = la::LengthEuclidean(distances_.size(), &distances_[0]);
if (unlikely(mem_bfgs_<0)) {
FATAL("You forgot to initialize the memory for BFGS\n");
}
@@ -122,7 +121,7 @@ void NonConvexMVU::ComputeLocalOptimumBFGS() {
previous_gradient_.CopyValues(gradient_);
previous_coordinates_.CopyValues(coordinates_);
ComputeFeasibilityError_(&distance_constraint, &centering_constraint);
NOTIFY("%li Feasibility error: %lg\n",i, distance_constraint);
NOTIFY("%i Feasibility error: %lg\n",i, distance_constraint);
}
NOTIFY("Now starting optimizing with BFGS...\n");
previous_feasibility_error_= distance_constraint + centering_constraint;
@@ -158,7 +157,8 @@ void NonConvexMVU::ComputeLocalOptimumBFGS() {
// step=la::DistanceSqEuclidean(new_dimension_ * num_of_points_,
// previous_coordinates_.ptr(), coordinates_.ptr());
if (step * norm_gradient/norm_coordinates < tolerance_){
if (step * norm_gradient/norm_coordinates < tolerance_ ||
distance_constraint < tolerance_){
break;
}
UpdateBFGS_();
@@ -166,11 +166,11 @@ void NonConvexMVU::ComputeLocalOptimumBFGS() {
previous_gradient_.CopyValues(gradient_);
}
UpdateBFGS_();
if (distance_constraint/sum_of_dist_square < tolerance_) {
if (distance_constraint < tolerance_) {
NOTIFY("Converged !!\n");
NOTIFY("Objective function: %lg\n", ComputeObjective_(coordinates_));
NOTIFY("Distances constraints: %lg, Centering constraint: %lg\n",
distance_constraint/sum_of_dist_square, centering_constraint);
distance_constraint, centering_constraint);
return;
}
//UpdateLagrangeMultStochastic_();