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Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation 被引量:4
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作者 Da-chuan XU~(1+) Shu-zhong ZHANG~2 1 Department of Applied Mathematics,Beijing University of Technology,Beijing 100022,China 2 Department of Systems Engineering and Engineering Management,The Chinese University of Hong Kong,Shatin,Hong Kong,China 《Science China Mathematics》 SCIE 2007年第11期1583-1596,共14页
In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the worst-case performance ratio at le... In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the worst-case performance ratio at leastα=0.87856….In fact,the estimated worst-case performance ratio is dependent on the data of the problem withαbeing a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach (with the triangle inequalities added) is at leastα+δ_d if every weight is strictly positive,whereδ_d>0 is a constant depending on the problem dimension and data. 展开更多
关键词 quadratic maximization max-cut problem semideflnite programming relaxation approximation algorithm performance ratio
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