Package org.hipparchus.optim.nonlinear.scalar.gradient

Class NonLinearConjugateGradientOptimizer.IdentityPreconditioner

java.lang.Object
org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.IdentityPreconditioner
All Implemented Interfaces:
Preconditioner
Enclosing class:
NonLinearConjugateGradientOptimizer
public static class NonLinearConjugateGradientOptimizer.IdentityPreconditioner extends Object implements Preconditioner
Default identity preconditioner.

  • Constructor Details

    • IdentityPreconditioner

      public IdentityPreconditioner()
      Empty constructor.

      This constructor is not strictly necessary, but it prevents spurious javadoc warnings with JDK 18 and later.

      Since:
      3.0

  • Method Details

    • precondition

      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
      Parameters:
      variables - current point at which the search direction was computed
      r - raw search direction (i.e. opposite of the gradient)
      Returns:
      approximation of H-1r where H is the objective function hessian