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High-dimensional stochastic optimal control using continuous tensor decompositions

Published:2018-02 Issue:2-3 Volume:37 Page:340-377
ISSN:0278-3649
Container-title:The International Journal of Robotics Research
language:en
Short-container-title:The International Journal of Robotics Research

Author:

Gorodetsky Alex¹^ORCID,Karaman Sertac¹,Marzouk Youssef¹

Affiliation:

1. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

U.S. Department of Energy

National Science Foundation

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modelling and Simulation,Software

Link

http://journals.sagepub.com/doi/pdf/10.1177/0278364917753994

Reference79 articles.

1. Tensor Networks and Hierarchical Tensors for the Solution of High-Dimensional Partial Differential Equations

2. An Extension of MATLAB to Continuous Functions and Operators

3. Applied Dynamic Programming

4. Adaptive Control Processes

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1. Consistent smooth approximation of feedback laws for infinite horizon control problems with non-smooth value functions;Journal of Differential Equations;2024-12

2. Approximation in the extended functional tensor train format;Advances in Computational Mathematics;2024-05-28

3. Optimal polynomial feedback laws for finite horizon control problems;Computers & Mathematics with Applications;2023-10

4. Neural network approximation of optimal controls for stochastic reaction–diffusion equations;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-09-01

5. State-dependent Riccati equation feedback stabilization for nonlinear PDEs;Advances in Computational Mathematics;2023-02

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