fr.cnes.sirius.patrius.math.optim.nonlinear.scalar

Class MultivariateFunctionPenaltyAdapter

java.lang.Object

fr.cnes.sirius.patrius.math.optim.nonlinear.scalar.MultivariateFunctionPenaltyAdapter

All Implemented Interfaces:

MultivariateFunction

public class MultivariateFunctionPenaltyAdapter
extends Object
implements MultivariateFunction

Adapter extending bounded MultivariateFunction to an unbouded domain using a penalty function.

This adapter can be used to wrap functions subject to simple bounds on parameters so they can be used by optimizers that do not directly support simple bounds.

The principle is that the user function that will be wrapped will see its parameters bounded as required, i.e when its value method is called with argument array point, the elements array will fulfill requirement lower[i] <= point[i] <= upper[i] for all i. Some of the components may be unbounded or bounded only on one side if the corresponding bound is set to an infinite value. The optimizer will not manage the user function by itself, but it will handle this adapter and it is this adapter that will take care the bounds are fulfilled. The adapter value(double[]) method will be called by the optimizer with unbound parameters, and the adapter will check if the parameters is within range or not. If it is in range, then the underlying user function will be called, and if it is not the value of a penalty function will be returned instead.

This adapter is only a poor-man's solution to simple bounds optimization constraints that can be used with simple optimizers like SimplexOptimizer. A better solution is to use an optimizer that directly supports simple bounds like CMAESOptimizer or BOBYQAOptimizer. One caveat of this poor-man's solution is that if start point or start simplex is completely outside of the allowed range, only the penalty function is used, and the optimizer may converge without ever entering the range.

Since:

3.0

Version:

$Id: MultivariateFunctionPenaltyAdapter.java 18108 2017-10-04 06:45:27Z bignon $

See Also:

MultivariateFunctionMappingAdapter

Constructor Summary

Constructors

Constructor and Description
MultivariateFunctionPenaltyAdapter(MultivariateFunction boundedIn, double[] lowerIn, double[] upperIn, double offsetIn, double[] scaleIn) Simple constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	value(double[] point) Computes the underlying function value from an unbounded point.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MultivariateFunctionPenaltyAdapter

public MultivariateFunctionPenaltyAdapter(MultivariateFunction boundedIn,
                                  double[] lowerIn,
                                  double[] upperIn,
                                  double offsetIn,
                                  double[] scaleIn)

Simple constructor.

When the optimizer provided points are out of range, the value of the penalty function will be used instead of the value of the underlying function. In order for this penalty to be effective in rejecting this point during the optimization process, the penalty function value should be defined with care. This value is computed as:

   penalty(point) = offset + ∑i[scale[i] * √|point[i]-boundary[i]|]
 

where indices i correspond to all the components that violates their boundaries.

So when attempting a function minimization, offset should be larger than the maximum expected value of the underlying function and scale components should all be positive. When attempting a function maximization, offset should be lesser than the minimum expected value of the underlying function and scale components should all be negative. minimization, and lesser than the minimum expected value of the underlying function when attempting maximization.

These choices for the penalty function have two properties. First, all out of range points will return a function value that is worse than the value returned by any in range point. Second, the penalty is worse for large boundaries violation than for small violations, so the optimizer has an hint about the direction in which it should search for acceptable points.

Parameters:

boundedIn - bounded function

lowerIn - lower bounds for each element of the input parameters array (some elements may be set to Double.NEGATIVE_INFINITY for unbounded values)

upperIn - upper bounds for each element of the input parameters array (some elements may be set to Double.POSITIVE_INFINITY for unbounded values)

offsetIn - base offset of the penalty function

scaleIn - scale of the penalty function

Throws:

DimensionMismatchException - if lower bounds, upper bounds and scales are not consistent, either according to dimension or to bounadary values

Method Detail

value

public double value(double[] point)

Computes the underlying function value from an unbounded point.

This method simply returns the value of the underlying function if the unbounded point already fulfills the bounds, and compute a replacement value using the offset and scale if bounds are violated, without calling the function at all.

Specified by:

value in interface MultivariateFunction

Parameters:

point - unbounded point

Returns:

either underlying function value or penalty function value