`pub struct NelderMead<P, F> { /* fields omitted */ }`

The Nelder-Mead method a heuristic search method for nonlinear optimization problems which does not require derivatives.

The method is based on simplices which consist of n+1 vertices for an optimization problem with n dimensions. The function to be optimized is evaluated at all vertices. Based on these cost function values the behaviour of the cost function is extrapolated in order to find the next point to be evaluated.

The following actions are possible:

1. Reflection: (Parameter `alpha`, default `1`)
2. Expansion: (Parameter `gamma`, default `2`)
3. Contraction: (Parameter `rho`, default `0.5`)
4. Shrink: (Parameter `sigma`, default `0.5`)

Example

# References:

Wikipedia

## Implementations

### `impl<P, F> NelderMead<P, F> where    P: Clone + Default + ArgminAdd<P, P> + ArgminSub<P, P> + ArgminMul<F, P>,    F: ArgminFloat, `[src]

Constructor

#### `pub fn sd_tolerance(self, tol: F) -> Self`[src]

Set Sample standard deviation tolerance

set alpha

set gamma

set rho

set sigma

## Blanket Implementations

### `impl<T> ToOwned for T where    T: Clone, `[src]

#### `type Owned = T`

The resulting type after obtaining ownership.

### `impl<T, U> TryFrom<U> for T where    U: Into<T>, `[src]

#### `type Error = Infallible`

The type returned in the event of a conversion error.

### `impl<T, U> TryInto<U> for T where    U: TryFrom<T>, `[src]

#### `type Error = <U as TryFrom<T>>::Error`

The type returned in the event of a conversion error.