NonLinearConjugateGradientOptimizer.IdentityPreconditioner (Hipparchus 3.0 API)

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 Summary
  
  Constructors
  Constructor Description
  
  IdentityPreconditioner()
  Empty constructor.
- Method Summary
  
  All Methods Instance Methods Concrete Methods
  Modifier and Type Method Description
  
  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
  - 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 Detail
  - 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