Stochastic optimal control

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• graycat · Mar 28, 2017 · Mentions

  …functions have some nice theoretical properties and can be useful in approximating some complicated data; e.g., IIRC D. Bertsekas at MIT used such fitting to approxmate the optimal value functions in stochastic optimal control. Or, have some big data and a lot of computing cycles? One way to soak them up is stochastic optimal control, possibly with some sigmoid fitting. To me, that's more promising for the future than what…

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• NY_USA_Hacker · May 25, 2011 · Mentions

  …elsewhere. For physics, yes, the deterministic theory of optimal control has been seen as replacing the older calculus of variations which goes back to Newton. My references are old. More recent work on stochastic optimal control has been by R. T. Rockafellar at University of Washington. For Stuart E. Dreyfus and Averill M. Law, 'The Art and Theory of Dynamic Programming', ISBN 0-12-221860-4, Academic Press, New York. 'dynamic…

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• graycat · Apr 20, 2018 · Mentions

  …then go nearly vertical, all supersonic, directly to 100,000 feet. For neural networks, IIRC there is some nice math that shows how general those can be for representing functions, and for some parts of stochastic optimal control Bertsekas proposed such a use for neural networks. There he, as usual, was being mathematical.

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• graycat · Dec 29, 2018 · Mentions

  …physics, especially Maxwell's equations. For stochastic processes, Doob, Karatsas and Shreve. For Princeton and its department of Operations Research and Financial Engineering, the more elementary parts of stochastic optimal control via, say, Nemhauser, Dreyfus and Law, Bertsekas and Shreve, Dynkin and Yushkevich, etc.

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