Randomized algorithms are a useful tool for analyzing the performance of complex uncertain systems. Their implementation requires the generation of a large number N of random samples representing the uncertainty scenarios, and the corresponding evaluation of system performance. When N is very large and/or performance evaluation is costly or time consuming, it can be necessary to distribute the computational burden of such algorithms among many cooperating computing units. This paper studies distributed versions of randomized algorithms for expected value and probability estimation over a network of computing nodes with possibly time-varying communication links. Explicit a-priori bounds are provided for the sample and communication complexity of these algorithms in terms of number of local samples, number of computing nodes and communication iterations.
Randomized Algorithms for Robustness Analysis: A Distributed Approach / Calafiore, Giuseppe Carlo. - STAMPA. - (2009), pp. 7030-7035. (Intervento presentato al convegno 48th IEEE Conference on Decision and Control tenutosi a Shanghai, China nel December 16-18, 2009) [10.1109/CDC.2009.5399948].
Randomized Algorithms for Robustness Analysis: A Distributed Approach
CALAFIORE, Giuseppe Carlo
2009
Abstract
Randomized algorithms are a useful tool for analyzing the performance of complex uncertain systems. Their implementation requires the generation of a large number N of random samples representing the uncertainty scenarios, and the corresponding evaluation of system performance. When N is very large and/or performance evaluation is costly or time consuming, it can be necessary to distribute the computational burden of such algorithms among many cooperating computing units. This paper studies distributed versions of randomized algorithms for expected value and probability estimation over a network of computing nodes with possibly time-varying communication links. Explicit a-priori bounds are provided for the sample and communication complexity of these algorithms in terms of number of local samples, number of computing nodes and communication iterations.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2284931
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