Interface VectorDifferentiableFunction
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- All Superinterfaces:
MultivariateVectorFunction
- All Known Subinterfaces:
Constraint
- All Known Implementing Classes:
BoundedConstraint
,EqualityConstraint
,InequalityConstraint
,LinearBoundedConstraint
,LinearEqualityConstraint
,LinearInequalityConstraint
public interface VectorDifferentiableFunction extends MultivariateVectorFunction
A MultivariateFunction that also has a defined gradient and Hessian.- Since:
- 3.1
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Method Summary
All Methods Instance Methods Abstract Methods Default Methods Modifier and Type Method Description int
dim()
Returns the dimensionality of the function domain.int
dimY()
Returns the dimensionality of the function eval.default RealMatrix
gradient(double[] x)
Returns the gradient of this function at (x)RealMatrix
jacobian(RealVector x)
Returns the gradient of this function at (x)default double[]
value(double[] x)
Returns the value of this function at (x)RealVector
value(RealVector x)
Returns the value of this function at (x)
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Method Detail
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dim
int dim()
Returns the dimensionality of the function domain. If dim() returns (n) then this function expects an n-vector as its input.- Returns:
- the expected dimension of the function's domain
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dimY
int dimY()
Returns the dimensionality of the function eval.- Returns:
- the expected dimension of the function's eval
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value
RealVector value(RealVector x)
Returns the value of this function at (x)- Parameters:
x
- a point to evaluate this function at.- Returns:
- the value of this function at (x)
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value
default double[] value(double[] x)
Returns the value of this function at (x)- Specified by:
value
in interfaceMultivariateVectorFunction
- Parameters:
x
- a point to evaluate this function at.- Returns:
- the value of this function at (x)
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jacobian
RealMatrix jacobian(RealVector x)
Returns the gradient of this function at (x)- Parameters:
x
- a point to evaluate this gradient at- Returns:
- the gradient of this function at (x)
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gradient
default RealMatrix gradient(double[] x)
Returns the gradient of this function at (x)- Parameters:
x
- a point to evaluate this gradient at- Returns:
- the gradient of this function at (x)
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