A Low-Rank Symplectic Gradient Adjustment Method for Computing Nash Equilibria
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911239628652544 |
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| author | Vater, Nadja Foglia, Katherine Rossella Colao, Vittorio Borzì, Alfio |
| author_facet | Vater, Nadja Foglia, Katherine Rossella Colao, Vittorio Borzì, Alfio |
| contents | This work presents a theoretical and numerical investigation of the symplectic gradient adjustment (SGA) method and of a low-rank SGA (LRSGA) method for efficiently solving two-objective optimization problems in the framework of Nash games. The SGA method outperforms the gradient method by including second-order mixed derivatives computed at each iterate, which requires considerably larger computational effort. For this reason, a LRSGA method is proposed where the approximation to second-order mixed derivatives are obtained by rank-one updates. The theoretical analysis presented in this work focuses on novel convergence estimates for the SGA and LRSGA methods, including parameter bounds. The superior computational complexity of the LRSGA method is demonstrated in the training of a CLIP neural architecture, where the LRSGA method outperforms the SGA method by orders of magnitude smaller CPU time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_25716 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Low-Rank Symplectic Gradient Adjustment Method for Computing Nash Equilibria Vater, Nadja Foglia, Katherine Rossella Colao, Vittorio Borzì, Alfio Optimization and Control Functional Analysis 49M15, 65H04, 65H10, 90C30, 47H05 This work presents a theoretical and numerical investigation of the symplectic gradient adjustment (SGA) method and of a low-rank SGA (LRSGA) method for efficiently solving two-objective optimization problems in the framework of Nash games. The SGA method outperforms the gradient method by including second-order mixed derivatives computed at each iterate, which requires considerably larger computational effort. For this reason, a LRSGA method is proposed where the approximation to second-order mixed derivatives are obtained by rank-one updates. The theoretical analysis presented in this work focuses on novel convergence estimates for the SGA and LRSGA methods, including parameter bounds. The superior computational complexity of the LRSGA method is demonstrated in the training of a CLIP neural architecture, where the LRSGA method outperforms the SGA method by orders of magnitude smaller CPU time. |
| title | A Low-Rank Symplectic Gradient Adjustment Method for Computing Nash Equilibria |
| topic | Optimization and Control Functional Analysis 49M15, 65H04, 65H10, 90C30, 47H05 |
| url | https://arxiv.org/abs/2510.25716 |