Thursday 28th of March 2024
 

A Tight Linearization Strategy for Zero-One Quadratic Programming Problems


Wajeb Gharibi and Yong Xia

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.

Keywords: Integer programming, quadratic programming, linearization.

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ABOUT THE AUTHORS

Wajeb Gharibi
1 Dept. of Computer Science, College of Computer Science & Information Systems, Jazan University, Jazan 82822-6694, KSA.

Yong Xia
School of Mathematics and System Sciences, Beihang University Beijing, 100191, P. R. China


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