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 }