EndCriteria::Type HybridSimulatedAnnealing<Sampler, Probability, Temperature, Reannealing>::minimize(Problem &P, const EndCriteria &endCriteria) {
EndCriteria::Type ecType = EndCriteria::None;
P.reset();
reannealing_.setProblem(P);
Array x = P.currentValue();
Size n = x.size();
Size k = 1;
Size kStationary = 1;
Size kReAnneal = 1;
Size kReset = 1;
Size maxK = endCriteria.maxIterations();
Size maxKStationary = endCriteria.maxStationaryStateIterations();
bool temperatureBreached = false;
Array currentTemperature(n, startTemperature_);
Array annealStep(n, 1.0);
Array bestPoint(x);
Array currentPoint(x);
Array startingPoint(x);
Array newPoint(x);
Real bestValue = P.value(bestPoint);
Real currentValue = bestValue;
Real startingValue = bestValue; //to reset to starting point if desired
while (k <= maxK && kStationary <= maxKStationary && !temperatureBreached)
{
//Draw a new sample point
sampler_(newPoint, currentPoint, currentTemperature);
//Evaluate new point
Real newValue = P.value(newPoint);
//Determine if new point is accepted
if (probability_(currentValue, newValue, currentTemperature)) {
if (optimizeScheme_ == EveryNewPoint) {
P.setCurrentValue(newPoint);
P.setFunctionValue(newValue);
localOptimizer_->minimize(P, endCriteria);
newPoint = P.currentValue();
newValue = P.functionValue();
}
currentPoint = newPoint;
currentValue = newValue;
}
//Check if we have a new best point
if (newValue < bestValue) {
if (optimizeScheme_ == EveryBestPoint) {
P.setCurrentValue(newPoint);
P.setFunctionValue(newValue);
localOptimizer_->minimize(P, endCriteria);
newPoint = P.currentValue();
newValue = P.functionValue();
}
kStationary = 0;
bestValue = newValue;
bestPoint = newPoint;
}
//Increase steps
k++;
kStationary++;
for (Size i = 0; i < annealStep.size(); i++)
annealStep[i]++;
//Reanneal if necessary
if (kReAnneal == reAnnealSteps_) {
kReAnneal = 0;
reannealing_(annealStep, currentPoint, currentValue, currentTemperature);
}
kReAnneal++;
//Reset if necessary
if (kReset == resetSteps_) {
kReset = 0;
switch (resetScheme_) {
case NoResetScheme:
break;
case ResetToBestPoint:
currentPoint = startingPoint;
currentValue = startingValue;
break;
case ResetToOrigin:
currentPoint = bestPoint;
currentValue = bestValue;
break;
}
}
kReset++;
//Update the current temperature according to current step
temperature_(currentTemperature, currentTemperature, annealStep);
//Check if temperature condition is breached
for (Size i = 0; i < n; i++)
temperatureBreached = temperatureBreached && currentTemperature[i] < endTemperature_;
}
//Change end criteria type if appropriate
if (k > maxK)
ecType = EndCriteria::MaxIterations;
else if (kStationary > maxKStationary)
ecType = EndCriteria::StationaryPoint;
//Set result to best point
P.setCurrentValue(bestPoint);
P.setFunctionValue(bestValue);
return ecType;
}
Real GoldsteinLineSearch::operator()(Problem& P,
EndCriteria::Type& ecType,
const EndCriteria& endCriteria,
const Real t_ini)
{
Constraint& constraint = P.constraint();
succeed_=true;
bool maxIter = false;
Real /*qtold,*/ t = t_ini; // see below, this is never used ?
Size loopNumber = 0;
Real q0 = P.functionValue();
Real qp0 = P.gradientNormValue();
Real tl = 0.0;
Real tr = 0.0;
qt_ = q0;
qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(gradient_,searchDirection_);
// Initialize gradient
gradient_ = Array(P.currentValue().size());
// Compute new point
xtd_ = P.currentValue();
t = update(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 (close_enough(tr, 0.0))
t *= extrapolation_;
else
t = (tl + tr) / 2.0;
// Store old value of the function
// qtold = qt_; // this is never used ?
// New point value
xtd_ = P.currentValue();
t = update(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, ecType);
if (maxIter)
break;
}
if (maxIter)
succeed_ = false;
// Compute new gradient
P.gradient(gradient_, xtd_);
// and it squared norm
qpt_ = DotProduct(gradient_, gradient_);
// Return new step value
return t;
}
EndCriteria::Type
LineSearchBasedMethod::minimize(Problem& P,
const EndCriteria& endCriteria) {
// Initializations
Real ftol = endCriteria.functionEpsilon();
Size maxStationaryStateIterations_
= endCriteria.maxStationaryStateIterations();
EndCriteria::Type ecType = EndCriteria::None; // reset end criteria
P.reset(); // reset problem
Array x_ = P.currentValue(); // store the starting point
Size iterationNumber_ = 0;
// dimension line search
lineSearch_->searchDirection() = Array(x_.size());
bool done = false;
// function and squared norm of gradient values;
Real fnew, fold, gold2;
Real fdiff;
// classical initial value for line-search step
Real t = 1.0;
// Set gradient g at the size of the optimization problem
// search direction
Size sz = lineSearch_->searchDirection().size();
Array prevGradient(sz), d(sz), sddiff(sz), direction(sz);
// Initialize cost function, gradient prevGradient and search direction
P.setFunctionValue(P.valueAndGradient(prevGradient, x_));
P.setGradientNormValue(DotProduct(prevGradient, prevGradient));
lineSearch_->searchDirection() = -prevGradient;
bool first_time = true;
// Loop over iterations
do {
// Linesearch
if (!first_time)
prevGradient = lineSearch_->lastGradient();
t = (*lineSearch_)(P, ecType, endCriteria, t);
// don't throw: it can fail just because maxIterations exceeded
//QL_REQUIRE(lineSearch_->succeed(), "line-search failed!");
if (lineSearch_->succeed())
{
// Updates
// New point
x_ = lineSearch_->lastX();
// New function value
fold = P.functionValue();
P.setFunctionValue(lineSearch_->lastFunctionValue());
// New gradient and search direction vectors
// orthogonalization coef
gold2 = P.gradientNormValue();
P.setGradientNormValue(lineSearch_->lastGradientNorm2());
// conjugate gradient search direction
direction = getUpdatedDirection(P, gold2, prevGradient);
sddiff = direction - lineSearch_->searchDirection();
lineSearch_->searchDirection() = direction;
// Now compute accuracy and check end criteria
// Numerical Recipes exit strategy on fx (see NR in C++, p.423)
fnew = P.functionValue();
fdiff = 2.0*std::fabs(fnew-fold) /
(std::fabs(fnew) + std::fabs(fold) + QL_EPSILON);
if (fdiff < ftol ||
endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
endCriteria.checkStationaryFunctionValue(0.0, 0.0,
maxStationaryStateIterations_, ecType);
endCriteria.checkMaxIterations(iterationNumber_, ecType);
return ecType;
}
P.setCurrentValue(x_); // update problem current value
++iterationNumber_; // Increase iteration number
first_time = false;
} else {
done = true;
}
} while (!done);
P.setCurrentValue(x_);
return ecType;
}
