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Globally solving nonconvex quadratic programming problems via completely positive programming (2011).
| Content Provider | CiteSeerX |
|---|---|
| Author | Chen, Jieqiu Burer, Samuel |
| Abstract | Nonconvex quadratic programming (QP) is an NP-hard problem that optimizes a general quadratic function over linear constraints. This paper introduces a new global optimization algorithm for this problem, which combines two ideas from the literature—finite branching based on the first-order KKT conditions and polyhedral-semidefinite relaxations of completely positive (or copositive) programs. Through a series of computational experiments comparing the new algorithm with existing codes on a diverse set of test instances, we demonstrate that the new algorithm is an attractive method for globally solving nonconvex QP. |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Nonconvex Quadratic Programming Problem Completely Positive Programming New Algorithm Computational Experiment Polyhedral-semidefinite Relaxation First-order Kkt Condition Np-hard Problem Attractive Method Literature Finite Diverse Set Test Instance Nonconvex Quadratic Programming Linear Constraint General Quadratic Function New Global Optimization Algorithm Nonconvex Qp |
| Content Type | Text |
| Resource Type | Article |
