Global optimization using random embeddings
We propose a random-subspace algorithmic framework for global optimization of Lipschitz-continuous objectives, and analyse its convergence using novel tools from conic integral geometry. X-REGO randomly projects, in a sequential or simultaneous manner, the high-dimensional original problem into low-...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2022
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