Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908346242564096 |
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| author | Yagishita, Shotaro |
| author_facet | Yagishita, Shotaro |
| contents | The generalized conditional gradient method is a popular algorithm for solving composite problems whose objective function is the sum of a smooth function and a nonsmooth convex function. Many convergence analyses of the algorithm rely on smoothness assumptions, such as the Lipschitz continuity of the gradient of the smooth part. This paper provides convergence results of linesearch-based generalized conditional gradient methods without smoothness assumptions. In particular, we show that a parameter-free variant, which automatically adapts to the Hölder exponent, guarantees convergence even when the gradient of the smooth part of the objective is not Hölder continuous. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01092 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Yagishita, Shotaro Optimization and Control The generalized conditional gradient method is a popular algorithm for solving composite problems whose objective function is the sum of a smooth function and a nonsmooth convex function. Many convergence analyses of the algorithm rely on smoothness assumptions, such as the Lipschitz continuity of the gradient of the smooth part. This paper provides convergence results of linesearch-based generalized conditional gradient methods without smoothness assumptions. In particular, we show that a parameter-free variant, which automatically adapts to the Hölder exponent, guarantees convergence even when the gradient of the smooth part of the objective is not Hölder continuous. |
| title | Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.01092 |