com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
Class AbsoluteError
java.lang.Object
com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
- All Implemented Interfaces:
- Function, RealScalarFunction
public class AbsoluteError
- extends MultiplierPenalty
This penalty function sums up the absolute error penalties.
It applies to equality constrained problems.
|
Method Summary |
double |
evaluate(double... x)
A real function, f, takes a double array double[],
{x0, x1, ..., xn}
and maps it to a double,
f(x0, x1, ..., xn) |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
AbsoluteError
public AbsoluteError(EqualityConstraints constraints,
double[] weights)
- Construct an AbsoluteError penalty function from a set of equality constraints.
- Parameters:
constraints - a set of equality constraintsweights - weights assigned to the constraints
AbsoluteError
public AbsoluteError(EqualityConstraints constraints,
double weights)
- Construct an AbsoluteError penalty function from a set of equality constraints.
We assign the same weight to all constraints.
- Parameters:
constraints - a set of equality constraintsweights - the same weight assigned to the constraints
AbsoluteError
public AbsoluteError(EqualityConstraints constraints)
- Construct an AbsoluteError penalty function from a set of equality constraints.
We assign the same default weight to all constraints.
- Parameters:
constraints - a set of equality constraints
evaluate
public double evaluate(double... x)
- Description copied from interface:
RealScalarFunction
- A real function,
f, takes a double array double[],
{x0, x1, ..., xn}
and maps it to a double,
f(x0, x1, ..., xn)
- Parameters:
x - {x} = {x0, x1, ... xn}
- Returns:
f({x}) as output
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