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On Risk-Averse Weighted k-Club Problems

被引：0
|
作者：
Rysz, Maciej [1 ]
Mahdavi Pajouh, Foad [2 ]
Krokhmal, Pavlo [1 ]
Pasiliao, Eduardo [3 ]
机构：
[1] Univ Iowa, Dept Mech & Ind Engn, 3131 Seamans Ctr, Iowa City, IA 52242 USA
[2] Univ Florida, Dept Ind & Syst Engn, Res & Engn Educ Facil UF REEF, Shalimar, FL 32579 USA
[3] US Air Force, Res Lab, Eglin AFB, FL 32542 USA
来源：
EXAMINING ROBUSTNESS AND VULNERABILITY OF NETWORKED SYSTEMS | 2014年 / 37卷
关键词：
k-club; clique relaxation; risk-averse subgraph problem; stochastic weights; coherent risk measures; MAXIMUM CLIQUE; FAST ALGORITHM; FLOW;
D O I：
10.3233/978-1-61499-391-9-231
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work, we consider a risk-averse maximum weighted k-club problems. It is assumed that vertices of the graph have stochastic weights whose joint distribution is known. The goal is to find the k-club of minimum risk contained in the graph. A stochastic programming framework that is based on the formalism of coherent risk measures is used to find the corresponding subgraphs. The selected representation of risk of a subgraph ensures that the optimal solutions are maximal k-clubs. A combinatorial branch-and-bound solution algorithm is proposed and solution performances are compared with an equivalent mathematical programming counterpart problem for instances with k = 2.
引用
收藏
页码：231 / 242
页数：12
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