Represents a one-dimensional optimizer based on Brent's algorithm using derivatives.

Represents a one-dimensional optimizer based on Brent's algorithm.

Represents an optimizer that uses a conjugate gradient algorithm.

Represents an optimizer that finds an extremum by searching along a search direction in each iteration.

Implements a one-dimensional optimizer that decreases the search interval by the Golden Ratio.

Represents a line search using the algorithm of Moré and Thuente.

Represents an algorithm for optimization of a multivariate function.

Implements the Nelder-Mead simplex algorithm for multi-dimensional optimization.

Serves as an abstract base class for classes that implement one-dimensional optimization algorithms.

Implements a line search algorithm that uses parabolic interpolation to locate the extremum.

Implements Powell's modified multidimensional optimization method.

Represents a multi-dimensional optimizer that uses a quasi-Newton algorithm (DFP or BFGS).

Implements a line search algorithm that always takes a unit step.

Enumerates the variants of the conjugate gradient method.

Enumerates the possible values for the type of extremum an optimizer is to look for.

Enumerates the variants of the quasi-Newton method.