Comparar métodos

	Programação Quadrática (QP)	Otimização Convexa
Área	Otimização	Otimização
Família	Process / pipeline	Process / pipeline
Ano de origem	1956	2004
Autor original	Marguerite Frank & Philip Wolfe	Stephen Boyd & Lieven Vandenberghe
Tipo	Constrained mathematical optimization	Mathematical optimization framework
Fonte seminal	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
Outros nomes	QP Optimization, Quadratic Optimization, Convex Quadratic Programming, İkinci Dereceden Programlama	Convex Programming, Disciplined Convex Programming, Dışbükey Optimizasyon, Convex Mathematical Programming
Relacionados	2	3
Resumo	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.