Comparer des méthodes

	Décomposition de Benders	Méthode du Lagrangien Augmenté	Méthode du Simplexe
Domaine	Recherche opérationnelle	Recherche opérationnelle	Recherche opérationnelle
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1962	1969	1947
Auteur d'origine	Jacques F. Benders	Magnus R. Hestenes and M. J. D. Powell	George Dantzig
Type	algorithm	algorithm	algorithm
Source fondatrice	Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238-252.	Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of Optimization Theory and Applications, 4(5), 303-320.	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.
Alias	cutting plane method, constraint generation	method of multipliers, augmented Lagrangian, ADMM	simplex algorithm
Apparentées	3	3	4
Résumé	Benders Decomposition, introduced by Jacques F. Benders in 1962, is a powerful algorithmic framework for solving large-scale mixed-integer programming (MIP) problems. It decomposes the problem into a master problem (controlling complicating variables) and subproblems (handling remaining variables), using cutting planes generated from subproblem dual information to iteratively tighten the master problem.	The Augmented Lagrangian Method, developed by Magnus R. Hestenes and M. J. D. Powell in 1969, is a powerful technique for solving constrained optimization problems. It converts a constrained problem into a sequence of unconstrained subproblems by augmenting the Lagrangian with a quadratic penalty term, enabling efficient solution of large-scale problems including convex and nonconvex cases.	The Simplex Method, developed by George Dantzig in 1947, is a foundational algorithm for solving linear programming problems. It systematically explores vertices of the feasible region to find the optimal solution where the objective function is maximized or minimized subject to linear constraints.