Parameter-free proximal bundle methods with adaptive stepsizes for hybrid convex composite optimization problems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910672809361408 |
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| author | Monteiro, Renato D. C. Zhang, Honghao |
| author_facet | Monteiro, Renato D. C. Zhang, Honghao |
| contents | This paper develops a parameter-free adaptive proximal bundle method with two important features: 1) adaptive choice of variable prox stepsizes that "closely fits" the instance under consideration; and 2) adaptive criterion for making the occurrence of serious steps easier. Computational experiments show that our method performs substantially fewer consecutive null steps (i.e., a shorter cycle) while maintaining the number of serious steps under control. As a result, our method performs significantly less number of iterations than its counterparts based on a constant prox stepsize choice and a non-adaptive cycle termination criterion. Moreover, our method is very robust relative to the user-provided initial stepsize. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20751 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter-free proximal bundle methods with adaptive stepsizes for hybrid convex composite optimization problems Monteiro, Renato D. C. Zhang, Honghao Optimization and Control This paper develops a parameter-free adaptive proximal bundle method with two important features: 1) adaptive choice of variable prox stepsizes that "closely fits" the instance under consideration; and 2) adaptive criterion for making the occurrence of serious steps easier. Computational experiments show that our method performs substantially fewer consecutive null steps (i.e., a shorter cycle) while maintaining the number of serious steps under control. As a result, our method performs significantly less number of iterations than its counterparts based on a constant prox stepsize choice and a non-adaptive cycle termination criterion. Moreover, our method is very robust relative to the user-provided initial stepsize. |
| title | Parameter-free proximal bundle methods with adaptive stepsizes for hybrid convex composite optimization problems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2410.20751 |