Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Optimization and Control
2019-08-22 v1
Abstract
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.
Cite
@article{arxiv.1908.07615,
title = {Iterative Linearized Control: Stable Algorithms and Complexity Guarantees},
author = {Vincent Roulet and Siddhartha Srinivasa and Dmitriy Drusvyatskiy and Zaid Harchaoui},
journal= {arXiv preprint arXiv:1908.07615},
year = {2019}
}
Comments
Short version appeared in International Conference on Machine Learning (ICML) 2019