org.apache.commons.math.optimization.general.Preconditioner Maven / Gradle / Ivy
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package org.apache.commons.math.optimization.general;
import org.apache.commons.math.FunctionEvaluationException;
/**
* This interface represents a preconditioner for differentiable scalar
* objective function optimizers.
* @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
* @since 2.0
*/
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
* @exception FunctionEvaluationException if no cost can be computed for the parameters
* @exception IllegalArgumentException if point dimension is wrong
*/
double[] precondition(double[] point, double[] r)
throws FunctionEvaluationException, IllegalArgumentException;
}