Mathematical Programming Computation, Volume 4, Issue 1, March 2012
Globally solving nonconvex quadratic programming problems via completely positive programming
Jieqiu Chen, Samuel Burer
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 polyhedralsemidefinite 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.
Full Text: PDF
Imprint and privacy statement
For the imprint and privacy statement we refer to the Imprint of ZIB.
© 2008-2020 by Zuse Institute Berlin (ZIB).