xref: /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/DifferentiableMultivariateVectorialOptimizer.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math.optimization;

import org.apache.commons.math.analysis.DifferentiableMultivariateVectorialFunction;
import org.apache.commons.math.FunctionEvaluationException;

/**
 * This interface represents an optimization algorithm for {@link DifferentiableMultivariateVectorialFunction
 * vectorial differentiable objective functions}.
 * <p>Optimization algorithms find the input point set that either {@link GoalType
 * maximize or minimize} an objective function.</p>
 * @see MultivariateRealOptimizer
 * @see DifferentiableMultivariateRealOptimizer
 * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
 * @since 2.0
 */
public interface DifferentiableMultivariateVectorialOptimizer {

    /** Set the maximal number of iterations of the algorithm.
     * @param maxIterations maximal number of function calls
     * .
     */
    void setMaxIterations(int maxIterations);

    /** Get the maximal number of iterations of the algorithm.
      * @return maximal number of iterations
     */
    int getMaxIterations();

    /** Get the number of iterations realized by the algorithm.
     * @return number of iterations
    */
   int getIterations();

   /** Set the maximal number of functions evaluations.
    * @param maxEvaluations maximal number of function evaluations
    */
   void setMaxEvaluations(int maxEvaluations);

   /** Get the maximal number of functions evaluations.
    * @return maximal number of functions evaluations
    */
   int getMaxEvaluations();

    /** Get the number of evaluations of the objective function.
     * <p>
     * The number of evaluation correspond to the last call to the
     * {@link #optimize(DifferentiableMultivariateVectorialFunction,
     * double[], double[], double[]) optimize} method. It is 0 if
     * the method has not been called yet.
     * </p>
     * @return number of evaluations of the objective function
     */
    int getEvaluations();

    /** Get the number of evaluations of the objective function jacobian .
     * <p>
     * The number of evaluation correspond to the last call to the
     * {@link #optimize(DifferentiableMultivariateVectorialFunction,
     * double[], double[], double[]) optimize} method. It is 0 if
     * the method has not been called yet.
     * </p>
     * @return number of evaluations of the objective function jacobian
     */
    int getJacobianEvaluations();

    /** Set the convergence checker.
     * @param checker object to use to check for convergence
     */
    void setConvergenceChecker(VectorialConvergenceChecker checker);

    /** Get the convergence checker.
     * @return object used to check for convergence
     */
    VectorialConvergenceChecker getConvergenceChecker();

    /** Optimizes an objective function.
     * <p>
     * Optimization is considered to be a weighted least-squares minimization.
     * The cost function to be minimized is
     * &sum;weight<sub>i</sub>(objective<sub>i</sub>-target<sub>i</sub>)<sup>2</sup>
     * </p>
     * @param f objective function
     * @param target target value for the objective functions at optimum
     * @param weights weight for the least squares cost computation
     * @param startPoint the start point for optimization
     * @return the point/value pair giving the optimal value for objective function
     * @exception FunctionEvaluationException if the objective function throws one during
     * the search
     * @exception OptimizationException if the algorithm failed to converge
     * @exception IllegalArgumentException if the start point dimension is wrong
     */
    VectorialPointValuePair optimize(DifferentiableMultivariateVectorialFunction f,
                                     double[] target, double[] weights,
                                     double[] startPoint)
        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException;

}