1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 package org.apache.commons.math.optimization.general; 19 20 import org.apache.commons.math.FunctionEvaluationException; 21 22 /** 23 * This interface represents a preconditioner for differentiable scalar 24 * objective function optimizers. 25 * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 fvr. 2011) $ 26 * @since 2.0 27 */ 28 public interface Preconditioner { 29 30 /** 31 * Precondition a search direction. 32 * <p> 33 * The returned preconditioned search direction must be computed fast or 34 * the algorithm performances will drop drastically. A classical approach 35 * is to compute only the diagonal elements of the hessian and to divide 36 * the raw search direction by these elements if they are all positive. 37 * If at least one of them is negative, it is safer to return a clone of 38 * the raw search direction as if the hessian was the identity matrix. The 39 * rationale for this simplified choice is that a negative diagonal element 40 * means the current point is far from the optimum and preconditioning will 41 * not be efficient anyway in this case. 42 * </p> 43 * @param point current point at which the search direction was computed 44 * @param r raw search direction (i.e. opposite of the gradient) 45 * @return approximation of H<sup>-1</sup>r where H is the objective function hessian 46 * @exception FunctionEvaluationException if no cost can be computed for the parameters 47 * @exception IllegalArgumentException if point dimension is wrong 48 */ 49 double[] precondition(double[] point, double[] r) 50 throws FunctionEvaluationException, IllegalArgumentException; 51 52 } 53
