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.math3.optimization.general;
019    
020    /**
021     * This interface represents a preconditioner for differentiable scalar
022     * objective function optimizers.
023     * @version $Id: Preconditioner.java 1422230 2012-12-15 12:11:13Z erans $
024     * @deprecated As of 3.1 (to be removed in 4.0).
025     * @since 2.0
026     */
027    @Deprecated
028    public interface Preconditioner {
029        /**
030         * Precondition a search direction.
031         * <p>
032         * The returned preconditioned search direction must be computed fast or
033         * the algorithm performances will drop drastically. A classical approach
034         * is to compute only the diagonal elements of the hessian and to divide
035         * the raw search direction by these elements if they are all positive.
036         * If at least one of them is negative, it is safer to return a clone of
037         * the raw search direction as if the hessian was the identity matrix. The
038         * rationale for this simplified choice is that a negative diagonal element
039         * means the current point is far from the optimum and preconditioning will
040         * not be efficient anyway in this case.
041         * </p>
042         * @param point current point at which the search direction was computed
043         * @param r raw search direction (i.e. opposite of the gradient)
044         * @return approximation of H<sup>-1</sup>r where H is the objective function hessian
045         */
046        double[] precondition(double[] point, double[] r);
047    }