/**
* Check if the optimization algorithm has converged considering the last two points. This method
* may be called several time from the same algorithm iteration with different points. This can be
* detected by checking the iteration number at each call if needed. Each time this method is
* called, the previous and current point correspond to points with the same role at each
* iteration, so they can be compared. As an example, simplex-based algorithms call this method
* for all points of the simplex, not only for the best or worst ones.
*
* @param iteration Index of current iteration
* @param previous Best point in the previous iteration.
* @param current Best point in the current iteration.
* @return {@code true} if the algorithm has converged.
*/
@Override
public boolean converged(
final int iteration, final PointValuePair previous, final PointValuePair current) {
if (maxIterationCount != ITERATION_CHECK_DISABLED && iteration >= maxIterationCount) {
return true;
}
final double p = previous.getValue();
final double c = current.getValue();
final double difference = FastMath.abs(p - c);
final double size = FastMath.max(FastMath.abs(p), FastMath.abs(c));
return difference <= size * getRelativeThreshold() || difference <= getAbsoluteThreshold();
}
public double estimateParameters() {
Sinv = new LUDecomposition(R).getSolver().getInverse();
WLSObjectiveFunction objectiveFunction = new WLSObjectiveFunction();
optimizer =
new NonLinearConjugateGradientOptimizer(
NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-8, 1e-8));
solution =
optimizer.optimize(
new MaxEval(1000),
objectiveFunction.getObjectiveFunction(),
objectiveFunction.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(getStartValues()),
new SimpleBounds(getLowerBounds(), getUpperBounds()));
computeFactorLoadings(solution.getPoint());
return solution.getValue();
}
