Package org.hipparchus.optim.nonlinear.scalar

Class GradientMultivariateOptimizer

  • Direct Known Subclasses:
    NonLinearConjugateGradientOptimizer
    public abstract class GradientMultivariateOptimizer
extends MultivariateOptimizer
    Base class for implementing optimizers for multivariate scalar differentiable functions. It contains boiler-plate code for dealing with gradient evaluation.

    • Constructor Detail

      • GradientMultivariateOptimizer

        protected GradientMultivariateOptimizer​(ConvergenceChecker<PointValuePair> checker)
        Simple constructor.
        Parameters:
        checker - Convergence checker.

    • Method Detail

      • computeObjectiveGradient

        protected double[] computeObjectiveGradient​(double[] params)
        Compute the gradient vector.
        Parameters:
        params - Point at which the gradient must be evaluated.
        Returns:
        the gradient at the specified point.

      • optimize

        public PointValuePair optimize​(OptimizationData... optData)
                        throws MathIllegalStateException
        Stores data and performs the optimization.

        The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.

        When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).

        Important note: Subclasses must override BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register their own options; but then, they must also call super.parseOptimizationData(optData) within that method.

        Overrides:
        optimize in class MultivariateOptimizer
        Parameters:
        optData - Optimization data. In addition to those documented in MultivariateOptimizer, this method will register the following data:
        Returns:
        a point/value pair that satisfies the convergence criteria.
        Throws:
        MathIllegalStateException - if the maximal number of evaluations (of the objective function) is exceeded.