针对洛瓦兹-施里弗 SDP 算子的具有高升降级的稳定集合多面体

IF 2.2 2区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Mathematical Programming Pub Date : 2024-05-25 DOI:10.1007/s10107-024-02093-0
Yu Hin Au, Levent Tunçel
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引用次数: 0

摘要

我们研究了相对于 Lovász-Schrijver SDP 算子 \({{\,textrm{LS}\,}}_+\) 的图的稳定集多边形的升降级。特别是,我们专注于寻找具有较高 \({{\,\textrm{LS}\,}}_+\)-rank 的相对较小的图形(即在分数稳定集合多面体上计算稳定集合多面体的 \({{\,\textrm{LS}\,}}_+\) 算子的迭代次数最少)。我们提供了一些图族,这些图的({{\textrm{LS}\,}}_+\)rank 近似是其顶点数的线性函数,这是在常数因子改进之前的最佳结果。这改进了 1999 年在这个方向上的最佳结果,当时得到的图\({\textrm{LS}\,}_+\)-rank 只随顶点数的平方根增长。
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Stable set polytopes with high lift-and-project ranks for the Lovász–Schrijver SDP operator

We study the lift-and-project rank of the stable set polytopes of graphs with respect to the Lovász–Schrijver SDP operator \({{\,\textrm{LS}\,}}_+\). In particular, we focus on a search for relatively small graphs with high \({{\,\textrm{LS}\,}}_+\)-rank (i.e., the least number of iterations of the \({{\,\textrm{LS}\,}}_+\) operator on the fractional stable set polytope to compute the stable set polytope). We provide families of graphs whose \({{\,\textrm{LS}\,}}_+\)-rank is asymptotically a linear function of its number of vertices, which is the best possible up to improvements in the constant factor. This improves upon the previous best result in this direction from 1999, which yielded graphs whose \({{\,\textrm{LS}\,}}_+\)-rank only grew with the square root of the number of vertices.

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来源期刊
Mathematical Programming
Mathematical Programming 数学-计算机:软件工程
CiteScore
5.70
自引率
11.10%
发文量
160
审稿时长
4-8 weeks
期刊介绍: Mathematical Programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variables, often subject to a set of constraints. This involves theoretical and computational issues as well as application studies. Included, along with the standard topics of linear, nonlinear, integer, conic, stochastic and combinatorial optimization, are techniques for formulating and applying mathematical programming models, convex, nonsmooth and variational analysis, the theory of polyhedra, variational inequalities, and control and game theory viewed from the perspective of mathematical programming.
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