How consensus-based optimization can be interpreted as a stochastic relaxation of gradient descent
We provide a novel analytical perspective on the theoretical understanding of gradient-based learning algorithms by interpreting consensus-based optimization (CBO), a recently proposed multi-particle derivative-free optimization method, as a stochastic relaxation of gradient descent. Remarkably, we...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2024
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