Globally Solving Nonconvex Quadratic Programming Problems via Completely Positive Programming

TitleGlobally Solving Nonconvex Quadratic Programming Problems via Completely Positive Programming
Publication TypeJournal Article
Year of Publication2012
AuthorsChen, J, Burer, S
JournalMathematical Programming Computation
Volume4
Issue1
Date Published03/2012
Other NumbersANL/MCS-P1967-1011
AbstractNonconvex 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.  
URLhttp://mpc.zib.de/index.php/MPC/article/view/79
PDFhttp://www.mcs.anl.gov/papers/P1967-1011.pdf