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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.09090 |
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| _version_ | 1866929276195962880 |
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| author | Zheng, Tianqi Loizou, Nicolas You, Pengcheng Mallada, Enrique |
| author_facet | Zheng, Tianqi Loizou, Nicolas You, Pengcheng Mallada, Enrique |
| contents | Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this problem, we introduce a dissipation term into the GDA updates to dampen these oscillations. The proposed Dissipative GDA (DGDA) method can be seen as performing standard GDA on a state-augmented and regularized saddle function that does not strictly introduce additional convexity/concavity. We theoretically show the linear convergence of DGDA in the bilinear and strongly convex-strongly concave settings and assess its performance by comparing DGDA with other methods such as GDA, Extra-Gradient (EG), and Optimistic GDA. Our findings demonstrate that DGDA surpasses these methods, achieving superior convergence rates. We support our claims with two numerical examples that showcase DGDA's effectiveness in solving saddle point problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09090 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization Zheng, Tianqi Loizou, Nicolas You, Pengcheng Mallada, Enrique Optimization and Control Machine Learning Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this problem, we introduce a dissipation term into the GDA updates to dampen these oscillations. The proposed Dissipative GDA (DGDA) method can be seen as performing standard GDA on a state-augmented and regularized saddle function that does not strictly introduce additional convexity/concavity. We theoretically show the linear convergence of DGDA in the bilinear and strongly convex-strongly concave settings and assess its performance by comparing DGDA with other methods such as GDA, Extra-Gradient (EG), and Optimistic GDA. Our findings demonstrate that DGDA surpasses these methods, achieving superior convergence rates. We support our claims with two numerical examples that showcase DGDA's effectiveness in solving saddle point problems. |
| title | Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2403.09090 |