A Sequential Quadratically Constrained Quadratic Programming Method for Differentiable Convex Minimization

This paper presents a sequential quadratically constrained quadratic programming (SQCQP) method for solving smooth convex programs. The SQCQP method solves at each iteration a subproblem that involves convex quadratic inequality constraints as well as a convex quadratic objective function. Such a qu...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:SIAM journal on optimization Ročník 13; číslo 4; s. 1098 - 1119
Hlavní autoři: Fukushima, Masao, Luo, Zhi-Quan, Tseng, Paul
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Society for Industrial and Applied Mathematics 01.01.2003
Témata:
Applied mathematics
Approximation
Lagrange multiplier
Methods
Quadratic programming
ISSN:1052-6234, 1095-7189
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper presents a sequential quadratically constrained quadratic programming (SQCQP) method for solving smooth convex programs. The SQCQP method solves at each iteration a subproblem that involves convex quadratic inequality constraints as well as a convex quadratic objective function. Such a quadratically constrained quadratic programming problem can be formulated as a second-order cone program, which can be solved efficiently by using interior point methods. We consider the following three fundamental issues on the SQCQP method: the feasibility of subproblems, the global convergence, and the quadratic rate of convergence. In particular, we show that the Maratos effect is avoided without any modification to the search direction, even though we use an ordinary $\ell_1$ exact penalty function as the line search merit function.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1052-6234
1095-7189
DOI:10.1137/S1052623401398120