Generalized conditional gradient methods for multiobjective composite optimization problems with H{ö}lder condition
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909361981358080 |
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| author | Chen, Wang Tang, Liping Yang, Xinmin |
| author_facet | Chen, Wang Tang, Liping Yang, Xinmin |
| contents | In this paper, we deal with multiobjective composite optimization problems, where each objective function is a combination of smooth and possibly non-smooth functions. We first propose a parameter-dependent conditional gradient method to solve this problem. The step size in this method requires prior knowledge of the parameters related to the H{ö}lder continuity of the gradient of the smooth function. The convergence properties of this method are then established. Given that these parameters may be unknown or, if known, may not be unique, the first method may encounter implementation challenges or slow convergence. To address this, we further propose a parameter-free conditional gradient method that determines the step size using a local quadratic upper approximation and an adaptive line search strategy, eliminating the need for any problem-specific parameters. The performance of the proposed methods is demonstrated on several test problems involving the indicator function and an uncertainty function. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18465 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalized conditional gradient methods for multiobjective composite optimization problems with H{ö}lder condition Chen, Wang Tang, Liping Yang, Xinmin Optimization and Control In this paper, we deal with multiobjective composite optimization problems, where each objective function is a combination of smooth and possibly non-smooth functions. We first propose a parameter-dependent conditional gradient method to solve this problem. The step size in this method requires prior knowledge of the parameters related to the H{ö}lder continuity of the gradient of the smooth function. The convergence properties of this method are then established. Given that these parameters may be unknown or, if known, may not be unique, the first method may encounter implementation challenges or slow convergence. To address this, we further propose a parameter-free conditional gradient method that determines the step size using a local quadratic upper approximation and an adaptive line search strategy, eliminating the need for any problem-specific parameters. The performance of the proposed methods is demonstrated on several test problems involving the indicator function and an uncertainty function. |
| title | Generalized conditional gradient methods for multiobjective composite optimization problems with H{ö}lder condition |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2410.18465 |