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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Minghua Chen Soung Chang Liew Ziyu Shao Caihong Kai |
| Copyright Year | 2010 |
| Abstract | Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distributed algorithms. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of time- reversible Markov chains. Certain carefully designed Markov chains among this class yield distributed algorithms that solve the log-sum-exp approximated combinatorial network optimization problem. By three case studies, we illustrate that Markov approximation technique not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in various domains with provable performance. We believe the Markov approximation framework will find applications in many network optimization problems, and this paper serves as a call for participation. |
| Starting Page | 1 |
| Ending Page | 9 |
| File Size | 200538 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781424458363 |
| ISSN | 0743166X |
| e-ISBN | 9781424458387 |
| DOI | 10.1109/INFCOM.2010.5461998 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-03-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Entropy Distributed algorithms Multiaccess communication Design optimization Algorithm design and analysis Throughput Communications Society Design engineering Network synthesis Probability distribution |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Electrical and Electronic Engineering |
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