Types in MathNet.Numerics.Optimization
Public Static Functions
IObjectiveFunction Gradient(Func<Vector<double>, ValueTuple<double, Vector<double>>> function)
Objective function where the Gradient is available. Greedy evaluation.
IObjectiveFunction Gradient(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient)
Objective function where the Gradient is available. Lazy evaluation.
IObjectiveFunction GradientHessian(Func<Vector<double>, ValueTuple<double, Vector<double>, Matrix<double>>> function)
Objective function where both Gradient and Hessian are available. Greedy evaluation.
IObjectiveFunction GradientHessian(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient, Func<Vector<double>, Matrix<double>> hessian)
Objective function where both Gradient and Hessian are available. Lazy evaluation.
IObjectiveFunction Hessian(Func<Vector<double>, ValueTuple<double, Matrix<double>>> function)
Objective function where the Hessian is available. Greedy evaluation.
IObjectiveFunction Hessian(Func<Vector<double>, double> function, Func<Vector<double>, Matrix<double>> hessian)
Objective function where the Hessian is available. Lazy evaluation.
IObjectiveFunction NonlinearFunction(Func<Vector<double>, Vector<double>, Vector<double>> function, Func<Vector<double>, Vector<double>, Matrix<double>> derivatives, Vector<T> observedX, Vector<T> observedY, Vector<T> weight)
Objective function with a user supplied jacobian for nonlinear least squares regression.
IObjectiveFunction NonlinearFunction(Func<Vector<double>, Vector<double>, Vector<double>> function, Vector<T> observedX, Vector<T> observedY, Vector<T> weight, int accuracyOrder)
Objective function for nonlinear least squares regression.
The numerical jacobian with accuracy order is used.
IObjectiveModel NonlinearModel(Func<Vector<double>, Vector<double>, Vector<double>> function, Func<Vector<double>, Vector<double>, Matrix<double>> derivatives, Vector<T> observedX, Vector<T> observedY, Vector<T> weight)
objective model with a user supplied jacobian for non-linear least squares regression.
IObjectiveModel NonlinearModel(Func<Vector<double>, Vector<double>, Vector<double>> function, Vector<T> observedX, Vector<T> observedY, Vector<T> weight, int accuracyOrder)
Objective model for non-linear least squares regression.
IScalarObjectiveFunction ScalarDerivative(Func<double, double> function, Func<double, double> derivative)
Objective function where the first derivative is available.
IScalarObjectiveFunction ScalarSecondDerivative(Func<double, double> function, Func<double, double> derivative, Func<double, double> secondDerivative)
Objective function where the first and second derivatives are available.
Objective function where neither first nor second derivative is available.
IObjectiveFunction Value(Func<Vector<double>, double> function)
Objective function where neither Gradient nor Hessian is available.