## Types in MathNet.Numerics.Optimization

Namespace MathNet.Numerics.Optimization

Interfaces IUnconstrainedMinimizer

Class implementing the Nelder-Mead simplex algorithm, used to find a minima when no gradient is available. Called fminsearch() in Matlab. A description of the algorithm can be found at http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11 or https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method

### Public Static Functions

#### MinimizationResultMinimum(IObjectiveFunction objectiveFunction, Vector<T> initialGuess, double convergenceTolerance, int maximumIterations)

Finds the minimum of the objective function without an initial perturbation, the default values used by fminsearch() in Matlab are used instead http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11
##### Parameters
###### `IObjectiveFunction` objectiveFunction

The objective function, no gradient or hessian needed

###### `Vector<T>` initialGuess

The initial guess

##### Return
###### `MinimizationResult`

The minimum point

#### MinimizationResultMinimum(IObjectiveFunction objectiveFunction, Vector<T> initialGuess, Vector<T> initalPertubation, double convergenceTolerance, int maximumIterations)

Finds the minimum of the objective function with an initial perturbation
##### Parameters
###### `IObjectiveFunction` objectiveFunction

The objective function, no gradient or hessian needed

###### `Vector<T>` initialGuess

The initial guess

###### `Vector<T>` initalPertubation

The initial perturbation

##### Return
###### `MinimizationResult`

The minimum point

### Public Methods

#### MinimizationResultFindMinimum(IObjectiveFunction objectiveFunction, Vector<T> initialGuess)

Finds the minimum of the objective function without an initial perturbation, the default values used by fminsearch() in Matlab are used instead http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11
##### Parameters
###### `IObjectiveFunction` objectiveFunction

The objective function, no gradient or hessian needed

###### `Vector<T>` initialGuess

The initial guess

##### Return
###### `MinimizationResult`

The minimum point

#### MinimizationResultFindMinimum(IObjectiveFunction objectiveFunction, Vector<T> initialGuess, Vector<T> initalPertubation)

Finds the minimum of the objective function with an initial perturbation
##### Parameters
###### `IObjectiveFunction` objectiveFunction

The objective function, no gradient or hessian needed

###### `Vector<T>` initialGuess

The initial guess

###### `Vector<T>` initalPertubation

The initial perturbation

##### Return
###### `MinimizationResult`

The minimum point