An Approximation Framework for Infinite Horizon Stochastic Dynamic Optimization Problems with Discounted Costs
نویسنده
چکیده
Traditional approaches to solving stochastic optimal control problems involve dynamic programming, and solving certain optimality equations. When recast as stochastic programming problems, structural aspects such as convexity are regained, and solution procedures based on decomposition and duality may be exploited. This paper explores a class of stationary, infinite-horizon stochastic optimization problems with discounted cost criterion in the framework of stochastic programming. Approximating techniques are developed, and intuitive lower bounds are obtained via averaging the future.
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