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General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming

Authors
Jaehyun Park, Stephen Boyd
Year
2017
Preprint
Identifiers
arXiv 1703.07870

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.

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