/**
* @param function
* @param lineSearcher
* @param guess
* @param direction
* @return
*/
protected double[] doLineSearch(
DifferentiableFunction function,
BacktrackingLineSearcher lineSearcher,
double[] guess,
double[] direction) {
return lineSearcher.minimize(function, guess, direction);
}
public double[] minimize(DifferentiableFunction function, double[] initial, double tolerance) {
boolean printProgress = true;
BacktrackingLineSearcher lineSearcher = new BacktrackingLineSearcher();
double[] guess = DoubleArrays.clone(initial);
// this grows linearly as the iterations go on (if stepSizeGrowthAmount
// > 0.0)
// but gets scaled back geometrically by lineSearcher
double stepSize = initialStepSize;
for (int iteration = 0; iteration < maxIterations; iteration++) {
if (stepSizeGrowthAmount == 0.0) stepSize = initialStepSize;
else {
stepSize *= stepSizeGrowthAmount;
}
double[] subgradient = function.derivativeAt(guess);
double value = function.valueAt(guess);
double[] direction = subgradient;
DoubleArrays.scale(direction, -1.0);
if (iteration == 0) lineSearcher.stepSizeMultiplier = initialStepSizeMultiplier;
else lineSearcher.stepSizeMultiplier = stepSizeMultiplier;
lineSearcher.initialStepSize = stepSize;
double[] nextGuess = doLineSearch(function, lineSearcher, guess, direction);
stepSize = lineSearcher.getFinalStepSize();
double[] nextDerivative = function.derivativeAt(nextGuess);
double nextValue = function.valueAt(nextGuess);
if (printProgress) {
Logger.i().logs("[Subgradient] Iteration %d: %.6f", iteration, nextValue);
}
if (iteration >= minIterations && converged(value, nextValue, tolerance)) {
return nextGuess;
}
guess = nextGuess;
value = nextValue;
subgradient = nextDerivative;
}
return guess;
}
