Bandit Convex Optimisation
Fuente:
arXiv
Salvato in:
| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912702493884416 |
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| author | Lattimore, Tor |
| author_facet | Lattimore, Tor |
| contents | Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. This book covers the many tools used for this problem, including cutting plane methods, interior point methods, continuous exponential weights, gradient descent and online Newton step. The nuances between the many assumptions and setups are explained. Although there is not much truly new here, some existing tools are applied in novel ways to obtain new algorithms. A few bounds are improved in minor ways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06535 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bandit Convex Optimisation Lattimore, Tor Optimization and Control Machine Learning Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. This book covers the many tools used for this problem, including cutting plane methods, interior point methods, continuous exponential weights, gradient descent and online Newton step. The nuances between the many assumptions and setups are explained. Although there is not much truly new here, some existing tools are applied in novel ways to obtain new algorithms. A few bounds are improved in minor ways. |
| title | Bandit Convex Optimisation |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2402.06535 |