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- W2017952273 abstract "Abstract In this article, we propose a feasible direction algorithm for solving the semidefinite programming (SDP) relaxations of quadratic {−1, 1} programming problems. This algorithm's distinguishing features are that it uses a low rank factorization and searches with a constant step-size. Its convergence is also proven. Finally, we report some numerical examples to compare our method with the low rank factorization method of Burer and Monteiro on the SDP relaxation of the max-cut problem. *E-mail: xdliuhongwei@hotmail.com Keywords: Quadratic {−1, 1} programmingLow rank factorizationFeasible direction algorithmSemidefinite programming Acknowledgments The authors are grateful to Samuel Burer for making his code freely available for comparison. We also thank the co-editor and two anonymous referees for their insightful comments and revisions on our article. The authors are also in debt to Hao Miao for helping write the C++ programme of our method. We also thank Shaanxi Province National Science Foundation (Grant No. 2001 SL05) for supporting this work. Notes *E-mail: xdliuhongwei@hotmail.com" @default.
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- W2017952273 title "Feasible direction algorithm for solving the SDP relaxations of quadratic {−1, 1} programming problems" @default.
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