Abstract
In this chapter, we first introduce the formal definitions of Monte Carlo and Las Vegas randomized algorithms. Then, we overview algorithms for analysis of uncertain systems, which are based on the Monte Carlo methods previously studied. Various meta-algorithms for probabilistic performance verification and probabilistic worst-case performance are introduced. For control design, subsequent chapters, which are highlighted here, discuss in detail feasibility and optimization of various convex and nonconvex control problems. Crucial steps for implementing these algorithms are the determination of an appropriate sample size and the construction of efficient algorithms for random sampling the uncertainty. The chapter ends with a discussion of the computational complexity of randomized algorithms.
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Tempo, R., Calafiore, G., Dabbene, F. (2013). Randomized Algorithms in Systems and Control. In: Randomized Algorithms for Analysis and Control of Uncertain Systems. Communications and Control Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-4610-0_10
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DOI: https://doi.org/10.1007/978-1-4471-4610-0_10
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4609-4
Online ISBN: 978-1-4471-4610-0
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