| public gradient ( Vector grad_f, Vector x ) : void | ||
| grad_f | Vector | |
| x | Vector | |
| return | void | |
//! Perform line search
public override double value(Problem P, ref EndCriteria.Type ecType, EndCriteria endCriteria, double t_ini)
{
//OptimizationMethod& method = P.method();
Constraint constraint = P.constraint();
succeed_ = true;
bool maxIter = false;
double qtold;
double t = t_ini;
int loopNumber = 0;
double q0 = P.functionValue();
double qp0 = P.gradientNormValue();
qt_ = q0;
qpt_ = (gradient_.Count == 0) ? qp0 : -Vector.DotProduct(gradient_, searchDirection_);
// Initialize gradient
gradient_ = new Vector(P.currentValue().Count);
// Compute new point
xtd_ = (Vector)P.currentValue().Clone();
t = update(ref xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value(xtd_);
// Enter in the loop if the criterion is not satisfied
if ((qt_ - q0) > -alpha_ * t * qpt_)
{
do
{
loopNumber++;
// Decrease step
t *= beta_;
// Store old value of the function
qtold = qt_;
// New point value
xtd_ = P.currentValue();
t = update(ref xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value(xtd_);
P.gradient(gradient_, xtd_);
// and it squared norm
maxIter = endCriteria.checkMaxIterations(loopNumber, ref ecType);
} while ((((qt_ - q0) > (-alpha_ * t * qpt_)) || ((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) && (!maxIter));
}
if (maxIter)
{
succeed_ = false;
}
// Compute new gradient
P.gradient(gradient_, xtd_);
// and it squared norm
qpt_ = Vector.DotProduct(gradient_, gradient_);
// Return new step value
return(t);
}
//! Perform line search
public override double value(Problem P, ref EndCriteria.Type ecType, EndCriteria endCriteria, double t_ini)
{
//OptimizationMethod& method = P.method();
Constraint constraint = P.constraint();
succeed_ = true;
bool maxIter = false;
double qtold;
double t = t_ini;
int loopNumber = 0;
double q0 = P.functionValue();
double qp0 = P.gradientNormValue();
qt_ = q0;
qpt_ = (gradient_.Count == 0) ? qp0 : -Vector.DotProduct(gradient_, searchDirection_);
// Initialize gradient
gradient_ = new Vector(P.currentValue().Count);
// Compute new point
xtd_ = (Vector)P.currentValue().Clone();
t = update(ref xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value(xtd_);
// Enter in the loop if the criterion is not satisfied
if ((qt_ - q0) > -alpha_ * t * qpt_) {
do {
loopNumber++;
// Decrease step
t *= beta_;
// Store old value of the function
qtold = qt_;
// New point value
xtd_ = P.currentValue();
t = update(ref xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value(xtd_);
P.gradient(gradient_, xtd_);
// and it squared norm
maxIter = endCriteria.checkMaxIterations(loopNumber, ref ecType);
} while ((((qt_ - q0) > (-alpha_ * t * qpt_)) || ((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) && (!maxIter));
}
if (maxIter)
succeed_ = false;
// Compute new gradient
P.gradient(gradient_, xtd_);
// and it squared norm
qpt_ = Vector.DotProduct(gradient_, gradient_);
// Return new step value
return t;
}