Global Continuous Optimization with Error Bound and Fast Convergence
Author(s)
Maruyama, Yu; Zheng, Xiaoyu; Kawaguchi, Kenji
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This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.
Date issued
2016-06Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Journal of Artificial Intelligence Research
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Kawaguchi, Kenji, Yu Maruyama and Xiaoyu Zheng. "Global Continuous Optimization with Error Bound and Fast Convergence." Journal of Articial Intelligence Research 56 (2016): 153-195.
Version: Final published version
ISSN
1943-5037
1076-9757