org.hipparchus.optim.nonlinear.scalar.gradient.Preconditioner Maven / Gradle / Ivy
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/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.optim.nonlinear.scalar.gradient;
/**
* This interface represents a preconditioner for differentiable scalar
* objective function optimizers.
*/
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);
}