ILNumerics - Technical Application Development
Assembly: ILNumerics.Toolboxes.Optimization (in ILNumerics.Toolboxes.Optimization.dll) Version: 5.5.0.0 (5.5.7503.3146)
An array of dimension numberOfLines(f) X numberOfColumns(x)
High precision numerical estimation of the jacobian matrix of the objective function at the position x
[ILNumerics Optimization Toolbox]
Namespace: ILNumerics.Toolboxes
Assembly: ILNumerics.Toolboxes.Optimization (in ILNumerics.Toolboxes.Optimization.dll) Version: 5.5.0.0 (5.5.7503.3146)
Syntax
public static RetArray<double> jacobian_prec( OptimizationObjectiveFunction<double> func, InArray<double> x, InArray<double> Fx = null )
Parameters
- func
- Type: ILNumerics.ToolboxesOptimizationObjectiveFunctionDouble
Objective function(s) - x
- Type: ILNumericsInArrayDouble
Vector giving the current position of evaluation - Fx (Optional)
- Type: ILNumericsInArrayDouble
Vector with the result of the evaluation of the functions at x
Return Value
Type: RetArrayDoubleAn array of dimension numberOfLines(f) X numberOfColumns(x)
Exceptions
Exception | Condition |
---|---|
ArgumentNullException | If x has a null component |
ArgumentOutOfRangeException | If f is not defined at x |
Remarks
This is a precise version of the jacobian estimation, using (forward+backward) finite differences with automatic step size tuning and Ridders' method of polynomial extrapolation.
If an input array is empty, an empty array will be returned.
[ILNumerics Optimization Toolbox]
Examples
Array<double> x0 = 10 * rand(10, 1); Array<double> grad = diag(Optimization.jacobian_prec(x => cos(x), x0, null));
See Also