Package org.apache.commons.math.optimization

Interface DifferentiableMultivariateRealOptimizer

All Known Implementing Classes:
AbstractScalarDifferentiableOptimizer, MultiStartDifferentiableMultivariateRealOptimizer, NonLinearConjugateGradientOptimizer, PowellOptimizer
public interface DifferentiableMultivariateRealOptimizer
This interface represents an optimization algorithm for scalar differentiable objective functions. Optimization algorithms find the input point set that either maximize or minimize an objective function.
Since:
2.0
See Also:

  • Method Details

    • setMaxIterations

      void setMaxIterations(int maxIterations)
      Set the maximal number of iterations of the algorithm.
      Parameters:
      maxIterations - maximal number of function calls

    • getMaxIterations

      int getMaxIterations()
      Get the maximal number of iterations of the algorithm.
      Returns:
      maximal number of iterations

    • getIterations

      int getIterations()
      Get the number of iterations realized by the algorithm.

      The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

      Returns:
      number of iterations

    • setMaxEvaluations

      void setMaxEvaluations(int maxEvaluations)
      Set the maximal number of functions evaluations.
      Parameters:
      maxEvaluations - maximal number of function evaluations

    • getMaxEvaluations

      int getMaxEvaluations()
      Get the maximal number of functions evaluations.
      Returns:
      maximal number of functions evaluations

    • getEvaluations

      int getEvaluations()
      Get 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.

      Returns:
      number of evaluations of the objective function

    • getGradientEvaluations

      int getGradientEvaluations()
      Get the number of evaluations of the objective function gradient.

      The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

      Returns:
      number of evaluations of the objective function gradient

    • setConvergenceChecker

      void setConvergenceChecker(RealConvergenceChecker checker)
      Set the convergence checker.
      Parameters:
      checker - object to use to check for convergence

    • getConvergenceChecker

      RealConvergenceChecker getConvergenceChecker()
      Get the convergence checker.
      Returns:
      object used to check for convergence

    • optimize

      RealPointValuePair optimize(DifferentiableMultivariateRealFunction f, GoalType goalType, double[] startPoint) throws FunctionEvaluationException, OptimizationException, IllegalArgumentException
      Optimizes an objective function.
      Parameters:
      f - objective function
      goalType - type of optimization goal: either GoalType.MAXIMIZE or GoalType.MINIMIZE
      startPoint - the start point for optimization
      Returns:
      the point/value pair giving the optimal value for objective function
      Throws:
      FunctionEvaluationException - if the objective function throws one during the search
      OptimizationException - if the algorithm failed to converge
      IllegalArgumentException - if the start point dimension is wrong