Global Continuous Optimization with Error Bound and Fast Convergence
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 wit...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for the Advancement of Artificial Intelligence
2017
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| Online Access: | http://hdl.handle.net/1721.1/107756 https://orcid.org/0000-0003-1839-7504 |