A Lagrangian–DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems

We propose an efficient computational method for linearly constrained quadratic optimization problems (QOPs) with complementarity constraints based on their Lagrangian and doubly nonnegative (DNN) relaxation and first-order algorithms. The simplified Lagrangian–completely positive programming (CPP)...

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Veröffentlicht in:	Mathematical programming Jg. 156; H. 1-2; S. 161 - 187
Hauptverfasser:	Kim, Sunyoung, Kojima, Masakazu, Toh, Kim-Chuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016 Springer Nature B.V
Schlagworte:	Algorithms Approximation Assignment problem Calculus of Variations and Optimal Control; Optimization Combinatorics Computation Computer programming Conics Equivalence Full Length Paper Knapsack problem Lower bounds Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical models Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Optimization techniques Programming Studies Theoretical Variables United States > US 90C20 The Lagrangian–conic relaxation Iterative solver 90C25 Bisection method 90C26 Linearly constrained quadratic optimization problems with complementarity constraints
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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