手法を比較

	Augmented Lagrangian Method	ベンダー分解	シンプレックス法
分野	オペレーションズ・リサーチ	オペレーションズ・リサーチ	オペレーションズ・リサーチ
系統	Machine learning	Machine learning	Machine learning
提唱年	1969	1962	1947
提唱者	Magnus R. Hestenes and M. J. D. Powell	Jacques F. Benders	George Dantzig
種類	algorithm	algorithm	algorithm
原典	Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of Optimization Theory and Applications, 4(5), 303-320.	Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238-252.	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.
別名	method of multipliers, augmented Lagrangian, ADMM	cutting plane method, constraint generation	simplex algorithm
関連	3	3	4
概要	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.	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 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.