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 * 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
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package org.apache.commons.math3.optimization.general;

/**
 * This interface represents a preconditioner for differentiable scalar
 * objective function optimizers.
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 2.0
 */
@Deprecated
public interface Preconditioner {
    /**
     * Precondition a search direction.
     * 

* The returned preconditioned search direction must be computed fast or * the algorithm performances will drop drastically. A classical approach * is to compute only the diagonal elements of the hessian and to divide * the raw search direction by these elements if they are all positive. * If at least one of them is negative, it is safer to return a clone of * the raw search direction as if the hessian was the identity matrix. The * rationale for this simplified choice is that a negative diagonal element * means the current point is far from the optimum and preconditioning will * not be efficient anyway in this case. *

* @param point current point at which the search direction was computed * @param r raw search direction (i.e. opposite of the gradient) * @return approximation of H-1r where H is the objective function hessian */ double[] precondition(double[] point, double[] r); }




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