Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908577340325888 |
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| author | Chen, Ziyi Yu, Peiran Huang, Heng |
| author_facet | Chen, Ziyi Yu, Peiran Huang, Heng |
| contents | This work aims to solve a stochastic nonconvex nonsmooth composite optimization problem. Previous works on composite optimization problem requires the major part to satisfy Lipschitz smoothness or some relaxed smoothness conditions, which excludes some machine learning examples such as regularized ReLU network and sparse support matrix machine. In this work, we focus on stochastic nonconvex composite optimization problem without any smoothness assumptions. In particular, we propose two new notions of approximate stationary points for such optimization problem and obtain finite-time convergence results of two zeroth-order algorithms to these two approximate stationary points respectively. Finally, we demonstrate that these algorithms are effective using numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization Chen, Ziyi Yu, Peiran Huang, Heng Optimization and Control Machine Learning This work aims to solve a stochastic nonconvex nonsmooth composite optimization problem. Previous works on composite optimization problem requires the major part to satisfy Lipschitz smoothness or some relaxed smoothness conditions, which excludes some machine learning examples such as regularized ReLU network and sparse support matrix machine. In this work, we focus on stochastic nonconvex composite optimization problem without any smoothness assumptions. In particular, we propose two new notions of approximate stationary points for such optimization problem and obtain finite-time convergence results of two zeroth-order algorithms to these two approximate stationary points respectively. Finally, we demonstrate that these algorithms are effective using numerical experiments. |
| title | Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2510.04446 |