org.apache.commons.math3.optim.nonlinear.scalar
public class MultivariateFunctionMappingAdapter extends Object implements MultivariateFunction
Adapter for mapping bounded MultivariateFunction
to unbounded ones.
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 map
the unbounded value to the bounded range using appropriate functions like
Sigmoid
for double bounded elements for example.
As the optimizer sees only unbounded parameters, it should be noted that the
start point or simplex expected by the optimizer should be unbounded, so the
user is responsible for converting his bounded point to unbounded by calling
boundedToUnbounded(double[])
before providing them to the optimizer.
For the same reason, the point returned by the BaseMultivariateOptimizer.optimize(int,
MultivariateFunction, org.apache.commons.math3.optimization.GoalType, double[])
method is unbounded. So to convert this point to bounded, users must call
unboundedToBounded(double[])
by themselves!
This adapter is only a poor man 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 poorman's solution is that behavior near the bounds may be
numerically unstable as bounds are mapped from infinite values.
Another caveat is that convergence values are evaluated by the optimizer with
respect to unbounded variables, so there will be scales differences when
converted to bounded variables.
MultivariateFunctionPenaltyAdapter
Constructor and Description 

MultivariateFunctionMappingAdapter(MultivariateFunction bounded,
double[] lower,
double[] upper)
Simple constructor.

Modifier and Type  Method and Description 

double[] 
boundedToUnbounded(double[] point)
Maps an array from bounded to unbounded.

double[] 
unboundedToBounded(double[] point)
Maps an array from unbounded to bounded.

double 
value(double[] point)
Compute the underlying function value from an unbounded point.

public MultivariateFunctionMappingAdapter(MultivariateFunction bounded, double[] lower, double[] upper)
bounded
 bounded functionlower
 lower bounds for each element of the input parameters array
(some elements may be set to Double.NEGATIVE_INFINITY
for
unbounded values)upper
 upper bounds for each element of the input parameters array
(some elements may be set to Double.POSITIVE_INFINITY
for
unbounded values)DimensionMismatchException
 if lower and upper bounds are not
consistent, either according to dimension or to valuespublic double[] unboundedToBounded(double[] point)
point
 Unbounded values.public double[] boundedToUnbounded(double[] point)
point
 Bounded values.public double value(double[] point)
This method simply bounds the unbounded point using the mappings set up at construction and calls the underlying function using the bounded point.
value
in interface MultivariateFunction
point
 unbounded valueunboundedToBounded(double[])
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