手法を比較

	Augmented Lagrangian Method	列生成法（ダンツィグ・ウルフ法）	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	1960	1947
提唱者	Magnus R. Hestenes and M. J. D. Powell	George B. Dantzig and Philip Wolfe	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.	Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111.	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.
別名	method of multipliers, augmented Lagrangian, ADMM	Dantzig-Wolfe decomposition, column generation method	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.	Column Generation, developed by George B. Dantzig and Philip Wolfe in 1960, is a powerful optimization technique for solving large-scale linear programming problems with special structure. Also known as Dantzig-Wolfe Decomposition, it decomposes the problem into a master problem (restricted to a subset of variables/columns) and a pricing subproblem (identifying new variables), iteratively improving the solution by introducing only relevant columns.	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.