%0 Journal Article
%J Mathematical Programming Computation
%D 2011
%T Globally Solving Nonconvex QPs via Completely Positive Programming
%A J. Chen
%A S. Burer
%X Nonconvex quadratic programming 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|nite 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.
%B Mathematical Programming Computation
%V 4
%P 33-52
%8 02/2011
%G eng
%U http://www.optimization-online.org/DB_FILE/2011/02/2945.pdf
%1 http://www.mcs.anl.gov/papers/P1837.pdf