General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming
Optimization and Control
2017-05-18 v2
Abstract
We introduce the Suggest-and-Improve framework for general nonconvex quadratically constrained quadratic programs (QCQPs). Using this framework, we generalize a number of known methods and provide heuristics to get approximate solutions to QCQPs for which no specialized methods are available. We also introduce an open-source Python package QCQP, which implements the heuristics discussed in the paper.
Keywords
Cite
@article{arxiv.1703.07870,
title = {General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming},
author = {Jaehyun Park and Stephen Boyd},
journal= {arXiv preprint arXiv:1703.07870},
year = {2017}
}
Comments
63 pages, 2 tables