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Showing content from http://accord-framework.net/docs/html/N_Accord_Math_Optimization.htm below:

Augmented Lagrangian method for constrained non-linear optimization.

Base class for gradient-based optimization methods.

Base class for least-squares optimizers implementing the

interface.

Base class for optimization methods.

Binary search root finding algorithm.

Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization method.

Inner status of the

optimization algorithm. This class contains implementation details that can change at any time.

Brent's root finding and minimization algorithms.

Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization method.

Constrained optimization by linear approximation.

Conjugate Gradient (CG) optimization method.

Extension methods on the

interface.

General Sequential Minimal Optimization algorithm for Quadratic Programming problems.

Gauss-Newton algorithm for solving Least-Squares problems.

Goldfarb-Idnani Quadratic Programming Solver.

Gradient Descent (GD) for unconstrained optimization.

Levenberg-Marquardt algorithm for solving Least-Squares problems.

Constraint with only linear terms.

Linear Constraint Collection.

Line Search Failed Exception.

Hungarian method for solving the assignment problem, also known as the Kuhn–Munkres algorithm or Munkres assignment algorithm.

Nelder-Mead simplex algorithm with support for bound constraints for non-linear, gradient-free optimization.

Non-linear Conjugate Gradient (WARNING: This code can not be used for commercial purposes. It is MANDATORY to check the accompanying license file for this particular module AND the source code for more details before you use this code).

Constraint with only linear terms.

Quadratic objective function.

Optimization progress event arguments.

Constraint with only quadratic terms.

Quadratic objective function.

Resilient Backpropagation method for unconstrained optimization.

Subplex

Simplified Trust Region Newton Method (TRON) for non-linear optimization.

This namespace contains different methods for solving both constrained and unconstrained optimization problems. For unconstrained optimization, methods available include Conjugate Gradient (CG), Bounded and Unbounded Broyden–Fletcher–Goldfarb–Shanno (BFGS), Resilient Backpropagation and a simplified implementation of the Trust Region Newton Method (TRON).

For constrained optimization problems, methods available include the Augmented Lagrangian method for general non-linear optimization, Cobyla for gradient-free non-linear optimization, and the Goldfarb-Idnani method for solving Quadratic Programming (QP) problems.

This namespace also contains optimizers specialized for least squares problems, such as Gauss Newton and the Levenberg-Marquart least squares solvers.

For univariate problems, standard search algorithms are also available, such as Brent and Binary search.

The namespace class diagram is shown below.

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