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  2. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    Simplex algorithm. In mathematical optimization, Dantzig 's simplex algorithm (or simplex method) is a popular algorithm for linear programming. [1] The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. [2] Simplices are not actually used in the method, but one interpretation of it is that it ...

  3. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    The Nelder–Mead method (also downhill simplex method, amoeba method, or polytope method) is a numerical method used to find the minimum or maximum of an objective function in a multidimensional space. It is a direct search method (based on function comparison) and is often applied to nonlinear optimization problems for which derivatives may ...

  4. Simplex noise - Wikipedia

    en.wikipedia.org/wiki/Simplex_noise

    Simplex noise is the result of an n -dimensional noise function comparable to Perlin noise ("classic" noise) but with fewer directional artifacts, in higher dimensions, and a lower computational overhead. Ken Perlin designed the algorithm in 2001 [1] to address the limitations of his classic noise function, especially in higher dimensions.

  5. Revised simplex method - Wikipedia

    en.wikipedia.org/wiki/Revised_simplex_method

    Revised simplex method. In mathematical optimization, the revised simplex method is a variant of George Dantzig 's simplex method for linear programming. The revised simplex method is mathematically equivalent to the standard simplex method but differs in implementation. Instead of maintaining a tableau which explicitly represents the ...

  6. Dantzig–Wolfe decomposition - Wikipedia

    en.wikipedia.org/wiki/Dantzig–Wolfe_decomposition

    Dantzig–Wolfe decomposition is an algorithm for solving linear programming problems with special structure. It was originally developed by George Dantzig and Philip Wolfe and initially published in 1960. [1] Many texts on linear programming have sections dedicated to discussing this decomposition algorithm. [2][3][4][5][6][7] Dantzig–Wolfe ...

  7. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    HiGHS is open-source software to solve linear programming (LP), mixed-integer programming (MIP), and convex quadratic programming (QP) models. [1] Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, JavaScript, Fortran, and C#. It has no external dependencies.

  8. Earth mover's distance - Wikipedia

    en.wikipedia.org/wiki/Earth_mover's_distance

    The EMD can be computed by solving an instance of transportation problem, using any algorithm for minimum-cost flow problem, e.g. the network simplex algorithm. The Hungarian algorithm can be used to get the solution if the domain D is the set {0, 1}. If the domain is integral, it can be translated for the same algorithm by representing ...

  9. Simplex - Wikipedia

    en.wikipedia.org/wiki/Simplex

    Simplex. The four simplexes that can be fully represented in 3D space. In geometry, a simplex (plural: simplexes or simplices) is a generalization of the notion of a triangle or tetrahedron to arbitrary dimensions. The simplex is so-named because it represents the simplest possible polytope in any given dimension.