//! Perform line search
public override double value(Problem P, ref EndCriteria.Type ecType, EndCriteria endCriteria, double t_ini)
{
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_ = 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(ref 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(ref 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, // Optimization problem
ref EndCriteria.Type ecType,
EndCriteria endCriteria,
double t_ini) // initial value of line-search step
{
Constraint constraint = P.constraint();
succeed_ = true;
bool maxIter = false;
double t = t_ini;
int loopNumber = 0;
double q0 = P.functionValue();
double qp0 = P.gradientNormValue();
double tl = 0.0;
double tr = 0.0;
qt_ = q0;
qpt_ = (gradient_.empty()) ? qp0 : -Vector.DotProduct(gradient_, searchDirection_);
// Initialize gradient
gradient_ = new Vector(P.currentValue().size());
// Compute new point
xtd_ = P.currentValue();
t = update(ref xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value(xtd_);
while ((qt_ - q0) < -beta_ * t * qpt_ || (qt_ - q0) > -alpha_ * t * qpt_)
{
if ((qt_ - q0) > -alpha_ * t * qpt_)
{
tr = t;
}
else
{
tl = t;
}
++loopNumber;
// calculate the new step
if (Utils.close_enough(tr, 0.0))
{
t *= extrapolation_;
}
else
{
t = (tl + tr) / 2.0;
}
// 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);
if (maxIter)
{
break;
}
}
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);
}
