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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2205.11093 |
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| _version_ | 1866910880743030784 |
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| author | Yoon, TaeHo Ryu, Ernest K. |
| author_facet | Yoon, TaeHo Ryu, Ernest K. |
| contents | Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nesterov's momentum-based acceleration. In this work, we show that these accelerated algorithms exhibit what we call the merging path (MP) property; the trajectories of these algorithms merge quickly. Using this novel MP property, we establish point convergence of existing accelerated minimax algorithms and derive new state-of-the-art algorithms for the strongly-convex-strongly-concave setup and for the prox-grad setup. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_11093 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accelerated Minimax Algorithms Flock Together Yoon, TaeHo Ryu, Ernest K. Optimization and Control Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nesterov's momentum-based acceleration. In this work, we show that these accelerated algorithms exhibit what we call the merging path (MP) property; the trajectories of these algorithms merge quickly. Using this novel MP property, we establish point convergence of existing accelerated minimax algorithms and derive new state-of-the-art algorithms for the strongly-convex-strongly-concave setup and for the prox-grad setup. |
| title | Accelerated Minimax Algorithms Flock Together |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2205.11093 |