Ergodic Control of Diffusion Processes

Nonfiction, Science & Nature, Mathematics, Probability, Statistics
Cover of the book Ergodic Control of Diffusion Processes by Ari Arapostathis, Vivek S. Borkar, Mrinal K. Ghosh, Cambridge University Press
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Author: Ari Arapostathis, Vivek S. Borkar, Mrinal K. Ghosh ISBN: 9781107085411
Publisher: Cambridge University Press Publication: November 17, 2011
Imprint: Cambridge University Press Language: English
Author: Ari Arapostathis, Vivek S. Borkar, Mrinal K. Ghosh
ISBN: 9781107085411
Publisher: Cambridge University Press
Publication: November 17, 2011
Imprint: Cambridge University Press
Language: English

This comprehensive volume on ergodic control for diffusions highlights intuition alongside technical arguments. A concise account of Markov process theory is followed by a complete development of the fundamental issues and formalisms in control of diffusions. This then leads to a comprehensive treatment of ergodic control, a problem that straddles stochastic control and the ergodic theory of Markov processes. The interplay between the probabilistic and ergodic-theoretic aspects of the problem, notably the asymptotics of empirical measures on one hand, and the analytic aspects leading to a characterization of optimality via the associated Hamilton–Jacobi–Bellman equation on the other, is clearly revealed. The more abstract controlled martingale problem is also presented, in addition to many other related issues and models. Assuming only graduate-level probability and analysis, the authors develop the theory in a manner that makes it accessible to users in applied mathematics, engineering, finance and operations research.

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This comprehensive volume on ergodic control for diffusions highlights intuition alongside technical arguments. A concise account of Markov process theory is followed by a complete development of the fundamental issues and formalisms in control of diffusions. This then leads to a comprehensive treatment of ergodic control, a problem that straddles stochastic control and the ergodic theory of Markov processes. The interplay between the probabilistic and ergodic-theoretic aspects of the problem, notably the asymptotics of empirical measures on one hand, and the analytic aspects leading to a characterization of optimality via the associated Hamilton–Jacobi–Bellman equation on the other, is clearly revealed. The more abstract controlled martingale problem is also presented, in addition to many other related issues and models. Assuming only graduate-level probability and analysis, the authors develop the theory in a manner that makes it accessible to users in applied mathematics, engineering, finance and operations research.

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