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Domain Decomposition for Stochastic Optimal Control

M. B. Horowitz, I. Papusha, and J. W. Burdick


This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimization to approximate the value function, requiring a high polynomial degree to capture local phenomena. To improve the scalability of the method to problems of interest, a domain decomposition scheme is presented. By using local approximations, lower degree polynomials become sufficient, and both local and global properties of the value function are captured. The domain of the problem is split into a non-overlapping partition, with added constraints ensuring C1 continuity. The Alternating Direction Method of Multipliers (ADMM) is used to optimize over each domain in parallel and ensure convergence on the boundaries of the partitions. This results in improved conditioning of the problem and allows for much larger and more complex problems to be addressed with improved performance.


M. B. Horowitz, I. Papusha, and J. W. Burdick “Domain Decomposition for Stochastic Optimal Control,” IEEE Conference on Decision and Control (CDC), pp. 1866–1873, Los Angeles, CA, December 15–17, 2014.