Class NonLinearConjugateGradientOptimizer.IdentityPreconditioner

  extended by org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.IdentityPreconditioner
All Implemented Interfaces:
Enclosing class:

public static class NonLinearConjugateGradientOptimizer.IdentityPreconditioner
extends Object
implements Preconditioner

Default identity preconditioner.

Constructor Summary
Method Summary
 double[] precondition(double[] variables, double[] r)
          Precondition a search direction.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail


public NonLinearConjugateGradientOptimizer.IdentityPreconditioner()
Method Detail


public double[] precondition(double[] variables,
                             double[] r)
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.

Specified by:
precondition in interface Preconditioner
variables - current point at which the search direction was computed
r - raw search direction (i.e. opposite of the gradient)
approximation of H-1r where H is the objective function hessian

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