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| 格式: | Preprint |
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2023
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| 主題: | |
| 在線閱讀: | https://arxiv.org/abs/2301.06339 |
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| _version_ | 1866909113691144192 |
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| author | Aubin-Frankowski, Pierre-Cyril Rudi, Alessandro |
| author_facet | Aubin-Frankowski, Pierre-Cyril Rudi, Alessandro |
| contents | Handling an infinite number of inequality constraints in infinite-dimensional spaces occurs in many fields, from global optimization to optimal transport. These problems have been tackled individually in several previous articles through kernel Sum-Of-Squares (kSoS) approximations. We propose here a unified theorem to prove convergence guarantees for these schemes. Pointwise inequalities are turned into equalities within a class of nonnegative kSoS functions. Assuming further that the functions appearing in the problem are smooth, focusing on pointwise equality constraints enables the use of scattering inequalities to mitigate the curse of dimensionality in sampling the constraints. Our approach is illustrated in learning vector fields with side information, here the invariance of a set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_06339 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Approximation of optimization problems with constraints through kernel Sum-Of-Squares Aubin-Frankowski, Pierre-Cyril Rudi, Alessandro Optimization and Control Machine Learning 46E22, 46N10, 90C26 Handling an infinite number of inequality constraints in infinite-dimensional spaces occurs in many fields, from global optimization to optimal transport. These problems have been tackled individually in several previous articles through kernel Sum-Of-Squares (kSoS) approximations. We propose here a unified theorem to prove convergence guarantees for these schemes. Pointwise inequalities are turned into equalities within a class of nonnegative kSoS functions. Assuming further that the functions appearing in the problem are smooth, focusing on pointwise equality constraints enables the use of scattering inequalities to mitigate the curse of dimensionality in sampling the constraints. Our approach is illustrated in learning vector fields with side information, here the invariance of a set. |
| title | Approximation of optimization problems with constraints through kernel Sum-Of-Squares |
| topic | Optimization and Control Machine Learning 46E22, 46N10, 90C26 |
| url | https://arxiv.org/abs/2301.06339 |