A Tight Linearization Strategy for Zero-One Quadratic Programming Problems
Data Structures and Algorithms
2012-04-23 v1 Optimization and Control
Abstract
In this paper, we present a new approach to linearizing zero-one quadratic minimization problem which has many applications in computer science and communications. Our algorithm is based on the observation that the quadratic term of zero-one variables has two equivalent piece-wise formulations, convex and concave cases. The convex piece-wise objective function and/or constraints play a great role in deducing small linearization. Further tight strategies are also discussed.
Cite
@article{arxiv.1204.4562,
title = {A Tight Linearization Strategy for Zero-One Quadratic Programming Problems},
author = {Wajeb gharibi and Yong Xia},
journal= {arXiv preprint arXiv:1204.4562},
year = {2012}
}
Comments
6 pages