Next-Generation Iterative Algorithms for Large-Scale Min-Max Optimization: Design and Analysis
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911316674871296 |
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| author | Choudhury, Sayantan |
| author_facet | Choudhury, Sayantan |
| contents | This thesis investigates the design of algorithms for solving min-max optimization problems, which form the mathematical foundation of many modern applications in machine learning, game theory, and optimization. This work offers new theoretical insights and practical algorithms that address the limitations of existing methods in various problem settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Next-Generation Iterative Algorithms for Large-Scale Min-Max Optimization: Design and Analysis Choudhury, Sayantan Optimization and Control This thesis investigates the design of algorithms for solving min-max optimization problems, which form the mathematical foundation of many modern applications in machine learning, game theory, and optimization. This work offers new theoretical insights and practical algorithms that address the limitations of existing methods in various problem settings. |
| title | Next-Generation Iterative Algorithms for Large-Scale Min-Max Optimization: Design and Analysis |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2512.12235 |