    001    /*
    002     * Licensed to the Apache Software Foundation (ASF) under one or more
    003     * contributor license agreements.  See the NOTICE file distributed with
    004     * this work for additional information regarding copyright ownership.
    005     * The ASF licenses this file to You under the Apache License, Version 2.0
    006     * (the "License"); you may not use this file except in compliance with
    007     * the License.  You may obtain a copy of the License at
    008     *
    009     *      http://www.apache.org/licenses/LICENSE-2.0
    010     *
    011     * Unless required by applicable law or agreed to in writing, software
    012     * distributed under the License is distributed on an "AS IS" BASIS,
    013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    014     * See the License for the specific language governing permissions and
    015     * limitations under the License.
    016     */
    017
    018    package org.apache.commons.math.optimization.general;
    019
    020    import org.apache.commons.math.FunctionEvaluationException;
    021
    022    /**
    023     * This interface represents a preconditioner for differentiable scalar
    024     * objective function optimizers.
    025     * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 f??vr. 2011) $
    026     * @since 2.0
    027     */
    028    public interface Preconditioner {
    029
    030        /**
    031         * Precondition a search direction.
    032         * <p>
    033         * The returned preconditioned search direction must be computed fast or
    034         * the algorithm performances will drop drastically. A classical approach
    035         * is to compute only the diagonal elements of the hessian and to divide
    036         * the raw search direction by these elements if they are all positive.
    037         * If at least one of them is negative, it is safer to return a clone of
    038         * the raw search direction as if the hessian was the identity matrix. The
    039         * rationale for this simplified choice is that a negative diagonal element
    040         * means the current point is far from the optimum and preconditioning will
    041         * not be efficient anyway in this case.
    042         * </p>
    043         * @param point current point at which the search direction was computed
    044         * @param r raw search direction (i.e. opposite of the gradient)
    045         * @return approximation of H<sup>-1</sup>r where H is the objective function hessian
    046         * @exception FunctionEvaluationException if no cost can be computed for the parameters
    047         * @exception IllegalArgumentException if point dimension is wrong
    048         */
    049        double[] precondition(double[] point, double[] r)
    050            throws FunctionEvaluationException, IllegalArgumentException;
    051
    052    }