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Splitting sequential Monte Carlo for efficient unreliability estimation of highly reliable networks
Highlights Estimating the terminal network reliability is difficult. The existing Lomonosov’s method is not accurate for many cases. We present a general modification to solve this problem. This modification is shown to outperform the current state-of-the-art.
Abstract Assessing the reliability of complex technological systems such as communication networks, transportation grids, and bridge networks is a difficult task. From a mathematical point of view, the problem of estimating network reliability belongs to the #P complexity class. As a consequence, no analytical solution for solving this problem in a reasonable time is known to exist and one has to rely on approximation techniques. In this paper we focus on a well-known sequential Monte Carlo algorithm — Lomonosov’s turnip method. Despite the fact that this method was shown to be efficient under some mild conditions, it is known to be inadequate for a stable estimation of the network reliability in a rare-event setting. To overcome this obstacle, we suggest a quite general combination of sequential Monte Carlo and multilevel splitting. The proposed method is shown to bring a significant variance reduction as compared to the turnip algorithm, is easy to implement and parallelize, and has a proven performance guarantee for certain network topologies.
Splitting sequential Monte Carlo for efficient unreliability estimation of highly reliable networks
Highlights Estimating the terminal network reliability is difficult. The existing Lomonosov’s method is not accurate for many cases. We present a general modification to solve this problem. This modification is shown to outperform the current state-of-the-art.
Abstract Assessing the reliability of complex technological systems such as communication networks, transportation grids, and bridge networks is a difficult task. From a mathematical point of view, the problem of estimating network reliability belongs to the #P complexity class. As a consequence, no analytical solution for solving this problem in a reasonable time is known to exist and one has to rely on approximation techniques. In this paper we focus on a well-known sequential Monte Carlo algorithm — Lomonosov’s turnip method. Despite the fact that this method was shown to be efficient under some mild conditions, it is known to be inadequate for a stable estimation of the network reliability in a rare-event setting. To overcome this obstacle, we suggest a quite general combination of sequential Monte Carlo and multilevel splitting. The proposed method is shown to bring a significant variance reduction as compared to the turnip algorithm, is easy to implement and parallelize, and has a proven performance guarantee for certain network topologies.
Splitting sequential Monte Carlo for efficient unreliability estimation of highly reliable networks
Vaisman, Radislav (author) / Kroese, Dirk P. (author) / Gertsbakh, Ilya B. (author)
Structural Safety ; 63 ; 1-10
2016-07-05
10 pages
Article (Journal)
Electronic Resource
English
Splitting sequential Monte Carlo for efficient unreliability estimation of highly reliable networks
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