An unconstrained approach for solving low rank SDP relaxations of {-1, 1} quadratic problems
Keywords:
semidefinite programming, low rank factorization, boolean quadratic problem, nonlinear programmingAbstract
We consider low-rank semidefinite programming (LRSDP) relaxations of {-1, 1} quadratic problems that can be formulated as the nonconvex nonlinear programming problem of minimizing a quadratic function subject to separable quadratic equality constraints. We prove the equivalence of the LRSDP problem with the unconstrained minimization of a new merit function and we define an efficient and globally convergent algorithm for finding critical points of the LRSDP problem. Finally, we test our code on an extended set of instances of the Max-Cut problem and we report comparisons with other existing codesDownloads
Published
14-12-2009
How to Cite
Grippo, L., Palagi, L., Piacentini, M., & Piccialli, V. (2009). An unconstrained approach for solving low rank SDP relaxations of {-1, 1} quadratic problems. Department of Computer and System Sciences Antonio Ruberti Technical Reports, 1(13). Retrieved from https://rosa.uniroma1.it/rosa00/index.php/dis_technical_reports/article/view/8856