Sammenlign metoder

	Kvadratisk programmering (QP)	Konveks optimering
Fagfelt	Optimering	Optimering
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1956	2004
Opphavsperson	Marguerite Frank & Philip Wolfe	Stephen Boyd & Lieven Vandenberghe
Type	Constrained mathematical optimization	Mathematical optimization framework
Opprinnelig kilde	Frank, M., & Wolfe, P. (1956). An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3(1–2), 95–110.	Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. ISBN: 978-0-521-83378-3
Alias	QP Optimization, Quadratic Optimization, Convex Quadratic Programming, İkinci Dereceden Programlama	Convex Programming, Disciplined Convex Programming, Dışbükey Optimizasyon, Convex Mathematical Programming
Relaterte	2	3
Sammendrag	Quadratic Programming (QP) is a class of constrained mathematical optimization in which the objective function is quadratic and the constraints are linear. Formalized by Frank and Wolfe (1956) through their gradient-based feasible-direction algorithm, QP is foundational in operations research, finance, machine learning, and engineering design wherever one must minimize a convex (or non-convex) quadratic cost subject to linear feasibility conditions.	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.