Class MultiStartUnivariateOptimizer

  • public class MultiStartUnivariateOptimizer
    extends UnivariateOptimizer
    Special implementation of the UnivariateOptimizer interface adding multi-start features to an existing optimizer.
    This class wraps an optimizer in order to use it several times in turn with different starting points (trying to avoid being trapped in a local extremum when looking for a global one).
    • Constructor Detail

      • MultiStartUnivariateOptimizer

        public MultiStartUnivariateOptimizer​(UnivariateOptimizer optimizer,
                                             int starts,
                                             org.apache.commons.rng.UniformRandomProvider generator)
        Create a multi-start optimizer from a single-start optimizer.
        optimizer - Single-start optimizer to wrap.
        starts - Number of starts to perform. If starts == 1, the optimize methods will return the same solution as optimizer would.
        generator - Random generator to use for restarts.
        NotStrictlyPositiveException - if starts < 1.
    • Method Detail

      • getEvaluations

        public int getEvaluations()
        Gets the number of evaluations of the objective function. The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.
        getEvaluations in class BaseOptimizer<UnivariatePointValuePair>
        the number of evaluations of the objective function.
      • getOptima

        public UnivariatePointValuePair[] getOptima()
        Gets all the optima found during the last call to optimize. The optimizer stores all the optima found during a set of restarts. The optimize method returns the best point only. This method returns all the points found at the end of each starts, including the best one already returned by the optimize method.
        The returned array as one element for each start as specified in the constructor. It is ordered with the results from the runs that did converge first, sorted from best to worst objective value (i.e in ascending order if minimizing and in descending order if maximizing), followed by null elements corresponding to the runs that did not converge. This means all elements will be null if the optimize method did throw an exception. This also means that if the first element is not null, it is the best point found across all starts.
        an array containing the optima.
        MathIllegalStateException - if optimize has not been called.