Сравнение на методи

	Колоногенериране (Dantzig-Wolfe)	Метод на разширените лагранжиани	Симплекс метод
Област	Изследване на операциите	Изследване на операциите	Изследване на операциите
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	1960	1969	1947
Създател	George B. Dantzig and Philip Wolfe	Magnus R. Hestenes and M. J. D. Powell	George Dantzig
Тип	algorithm	algorithm	algorithm
Основополагащ източник	Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111.	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.
Други названия	Dantzig-Wolfe decomposition, column generation method	method of multipliers, augmented Lagrangian, ADMM	simplex algorithm
Свързани	3	3	4
Резюме	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 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.