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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

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.

Author Information

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

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