Abstract
We introduce a new relaxation framework for nonconvex quadratically constrained quadratic programs (QCQPs). In contrast to existing relaxations based on semidefinite programming (SDP), our relaxations incorporate features of both SDP and second order cone programming (SOCP) and, as a result, solve more quickly than SDP. A downside is that the calculated bounds are weaker than those gotten by SDP. The framework allows one to choose a block-diagonal structure for the mixed SOCP-SDP, which in turn allows one to control the speed and bound quality. For a fixed block-diagonal structure, we also introduce a procedure to improve the bound quality without increasing computation time significantly. The effectiveness of our framework is illustrated on a large sample of QCQPs from various sources.
| Original language | English |
|---|---|
| Pages (from-to) | 27-45 |
| Number of pages | 19 |
| Journal | Computational Optimization and Applications |
| Volume | 59 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - Oct 2014 |
Bibliographical note
Funding Information:The research of S. Burer was supported in part by NSF Grant CCF-0545514. The research of S. Kim was supported by NRF 2012-R1A1A2-038982 and NRF 2010-000-8784. The research of M. Kojima was partially supported by the Japan Science and Technology Agency (JST), the Core Research of Evolutionary Science and Technology (CREST) Research Project.
Keywords
- Difference of convex
- Nonconvex quadratic programming
- Second-order cone programming
- Semidefinite programming