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Poster
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet · Dmitriy Drusvyatskiy · Siddhartha Srinivasa · Zaid Harchaoui

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #39
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We examine popular gradient-based algorithms for nonlinear control in the light of the modern complexity analysis of first-order optimization algorithms. The examination reveals that the complexity bounds can be clearly stated in terms of calls to a computational oracle related to dynamic programming and implementable by gradient back-propagation using machine learning software libraries such as PyTorch or TensorFlow. Finally, we propose a regularized Gauss-Newton algorithm enjoying worst-case complexity bounds and improved convergence behavior in practice. The software library based on PyTorch is publicly available.

Keywords: Non-convex Optimization · Optimization - Others · Planning and Control · Robotics

Author Information

Vincent Roulet (University of Washington)
Dima Drusvyatskiy (University of Washington)
Siddhartha Srinivasa (University of Washington)
Zaid Harchaoui (University of Washington)

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