Convex reformulation for binary quadratic programming problems via average objective value maximization

Quadratic convex reformulation is an important method for improving the performance of a branch-and-bound based binary quadratic programming solver. In this paper, we study a new convex reformulation method. By this reformulation, the efficiency of a branch-and-bound algorithm can be improved signif...

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Vydáno v:Optimization letters Ročník 9; číslo 3; s. 523 - 535
Hlavní autoři: Lu, Cheng, Guo, Xiaoling
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2015
Témata:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Lower bounds
Branch-and-bound
Binary quadratic programming
Convex reformulation
ISSN:1862-4472, 1862-4480
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Popis
Shrnutí:Quadratic convex reformulation is an important method for improving the performance of a branch-and-bound based binary quadratic programming solver. In this paper, we study a new convex reformulation method. By this reformulation, the efficiency of a branch-and-bound algorithm can be improved significantly. We also compare this new reformulation method with other proposed methods, whose effectiveness has been proven. Numerical experimental results show that our reformulation method performs better than the compared methods for certain types of binary quadratic programming problems.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-014-0768-0