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针对最密集子图问题的双非负和半定松弛

Doubly Nonnegative and Semidefinite Relaxations for the Densest -Subgraph Problem.

作者信息

Guo Chuan-Hao, Guo Yuan, Liu Bei-Bei

机构信息

School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China.

出版信息

Entropy (Basel). 2019 Jan 24;21(2):108. doi: 10.3390/e21020108.

Abstract

The densest -subgraph (DkS) maximization problem is to find a set of vertices with maximum total weight of edges in the subgraph induced by this set. This problem is in general NP-hard. In this paper, two relaxation methods for solving the DkS problem are presented. One is doubly nonnegative relaxation, and the other is semidefinite relaxation with tighter relaxation compare with the relaxation of standard semidefinite. The two relaxation problems are equivalent under the suitable conditions. Moreover, the corresponding approximation ratios' results are given for these relaxation problems. Finally, some numerical examples are tested to show the comparison of these relaxation problems, and the numerical results show that the doubly nonnegative relaxation is more promising than the semidefinite relaxation for solving some DkS problems.

摘要

最密集子图(DkS)最大化问题是要找到一组顶点,使得由该组顶点所诱导出的子图中边的总权重最大。该问题一般来说是NP难问题。本文提出了两种用于求解DkS问题的松弛方法。一种是双非负松弛,另一种是与标准半定松弛相比具有更紧松弛的半定松弛。在合适的条件下,这两个松弛问题是等价的。此外,还给出了这些松弛问题相应的近似比率结果。最后,通过一些数值例子来测试这些松弛问题的比较情况,数值结果表明,对于求解某些DkS问题,双非负松弛比半定松弛更具前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6e5/7514591/b909de827b98/entropy-21-00108-g001.jpg

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