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Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization

Guanghui Lan (george.lan***at***isye.gatech.edu)
Zhiqiang Zhou (zzhoubrian***at***gatech.edu)

Abstract: In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA can achieve an optimal ${\cal O}(1/\epsilon^4)$ rate of convergence in terms of the total number of required scenarios when applied to a three-stage stochastic optimization problem. We further show that this rate of convergence can be improved to ${\cal O}(1/\epsilon^2)$ when the objective function is strongly convex. We also discuss variants of DSA for solving more general multi-stage stochastic optimization problems with the number of stages $T > 3$. The developed DSA algorithms only need to go through the scenario tree once in order to compute an $\epsilon$-solution of the multi-stage stochastic optimization problem. To the best of our knowledge, this is the first time that stochastic approximation type methods are generalized for multi-stage stochastic optimization with $T \ge 3$.

Keywords: Stochastic approximation, multi-stage stochastic optimization, stochastic dynamic programming

Category 1: Convex and Nonsmooth Optimization

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 07/11/2017
Entry Accepted: 07/11/2017
Entry Last Modified: 07/11/2017

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