Compară metode

	Optimizare Convexă	Programare Dinamică
Domeniu	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline
Anul apariției	2004	1957
Autorul original	Stephen Boyd & Lieven Vandenberghe	Richard Bellman
Tip	Mathematical optimization framework	Exact combinatorial optimization via recursive decomposition
Sursa seminală	Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. ISBN: 978-0-521-83378-3	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6
Denumiri alternative	Convex Programming, Disciplined Convex Programming, Dışbükey Optimizasyon, Convex Mathematical Programming	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama
Înrudite	3	3
Rezumat	Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Formalized and popularized by Stephen Boyd and Lieven Vandenberghe in their landmark 2004 textbook, the framework unifies a wide family of problems — including linear programming, quadratic programming, semidefinite programming, and second-order cone programming — under a single theoretical roof. Its defining property is that any locally optimal solution is also globally optimal, making it tractable and reliable for engineering, statistics, machine learning, and operations research.	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.