MPC

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).