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About Lecture Notes Stochastic Optimization-Koole
The document discusses stochastic optimization and Markov decision processes. It begins with an introduction to stochastic optimization, which involves dynamic decision problems where decisions over time have consequences later. The main solution technique is dynamic programming. Examples given include admission control to a queue and routing in a call center. The document then provides a refresher on probability theory concepts like the Poisson process and exponential distribution that are important for stochastic optimization. It introduces Markov chains and describes how to compute the limiting stationary distribution using forward recursion, assuming the Markov chain is unichain, aperiodic, and has a finite state space.
Detailed Information
Author: | ['nstl0101'] |
---|---|
Publication Year: | 2013 |
Pages: | 42 |
Language: | English |
Format: | |
Price: | FREE |
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