Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910246928121856 |
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| author | Jung, Kiyuob Oh, Jehan |
| author_facet | Jung, Kiyuob Oh, Jehan |
| contents | In this paper, we find the special case of the subgradient method minimizing a one-dimensional real-valued function, which we term the specular gradient method, that converges root-linearly without any additional assumptions except the convexity. Furthermore, we suggest a way to implement the specular gradient method without explicitly calculating specular derivatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence Jung, Kiyuob Oh, Jehan Optimization and Control Numerical Analysis 90C25, 49J52, 65K05, 26A27 In this paper, we find the special case of the subgradient method minimizing a one-dimensional real-valued function, which we term the specular gradient method, that converges root-linearly without any additional assumptions except the convexity. Furthermore, we suggest a way to implement the specular gradient method without explicitly calculating specular derivatives. |
| title | Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence |
| topic | Optimization and Control Numerical Analysis 90C25, 49J52, 65K05, 26A27 |
| url | https://arxiv.org/abs/2412.20747 |