## Types in MathNet.Numerics.Optimization

Type ObjectiveFunction

Namespace MathNet.Numerics.Optimization

### Public Static Functions

Objective function where the Gradient is available. Greedy evaluation.

Objective function where the Gradient is available. Lazy evaluation.

#### IObjectiveFunctionGradientHessian(Func<Vector<double>, ValueTuple<double, Vector<double>, Matrix<double>>> function)

Objective function where both Gradient and Hessian are available. Greedy evaluation.

Objective function where both Gradient and Hessian are available. Lazy evaluation.

#### IObjectiveFunctionHessian(Func<Vector<double>, ValueTuple<double, Matrix<double>>> function)

Objective function where the Hessian is available. Greedy evaluation.

#### IObjectiveFunctionHessian(Func<Vector<double>, double> function, Func<Vector<double>, Matrix<double>> hessian)

Objective function where the Hessian is available. Lazy evaluation.

#### IObjectiveFunctionNonlinearFunction(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.

#### IObjectiveFunctionNonlinearFunction(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.

#### IObjectiveModelNonlinearModel(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.

#### IObjectiveModelNonlinearModel(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.

#### IScalarObjectiveFunctionScalarDerivative(Func<double, double> function, Func<double, double> derivative)

Objective function where the first derivative is available.

#### IScalarObjectiveFunctionScalarSecondDerivative(Func<double, double> function, Func<double, double> derivative, Func<double, double> secondDerivative)

Objective function where the first and second derivatives are available.

#### IScalarObjectiveFunctionScalarValue(Func<double, double> function)

Objective function where neither first nor second derivative is available.

#### IObjectiveFunctionValue(Func<Vector<double>, double> function)

Objective function where neither Gradient nor Hessian is available.