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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.06737 |
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| _version_ | 1866911213095485440 |
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| author | Huang, Chengzhi |
| author_facet | Huang, Chengzhi |
| contents | In this paper, we propose a novel extrapolation coefficient scheme within a new extrapolation term and develop an accelerated proximal gradient algorithm. We establish that the algorithm achieves a sublinear convergence rate. The proposed scheme only requires the Lipschitz constant estimate sequence to satisfy mild initial conditions, under which a key equality property can be derived to support the convergence analysis. Numerical experiments are provided to demonstrate the effectiveness and practical performance of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast Accelerated Proximal Gradient Method with New Extrapolation Term for Multiobjective Optimization Huang, Chengzhi Optimization and Control In this paper, we propose a novel extrapolation coefficient scheme within a new extrapolation term and develop an accelerated proximal gradient algorithm. We establish that the algorithm achieves a sublinear convergence rate. The proposed scheme only requires the Lipschitz constant estimate sequence to satisfy mild initial conditions, under which a key equality property can be derived to support the convergence analysis. Numerical experiments are provided to demonstrate the effectiveness and practical performance of the proposed method. |
| title | Fast Accelerated Proximal Gradient Method with New Extrapolation Term for Multiobjective Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2507.06737 |