Globally Solving Nonconvex QPs via Completely Positive Programming
|Title||Globally Solving Nonconvex QPs via Completely Positive Programming|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Chen, J, Burer, S|
|Journal||Mathematical Programming Computation|
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.