Learning Parametric Convex Functions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913875238060032 |
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| author | Schaller, Maximilian Bemporad, Alberto Boyd, Stephen |
| author_facet | Schaller, Maximilian Bemporad, Alberto Boyd, Stephen |
| contents | A parametrized convex function depends on a variable and a parameter, and is convex in the variable for any valid value of the parameter. Such functions can be used to specify parametrized convex optimization problems, i.e., a convex optimization family, in domain specific languages for convex optimization. In this paper we address the problem of fitting a parametrized convex function that is compatible with disciplined programming, to some given data. This allows us to fit a function arising in a convex optimization formulation directly to observed or simulated data. We demonstrate our open-source implementation on several examples, ranging from illustrative to practical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Parametric Convex Functions Schaller, Maximilian Bemporad, Alberto Boyd, Stephen Optimization and Control A parametrized convex function depends on a variable and a parameter, and is convex in the variable for any valid value of the parameter. Such functions can be used to specify parametrized convex optimization problems, i.e., a convex optimization family, in domain specific languages for convex optimization. In this paper we address the problem of fitting a parametrized convex function that is compatible with disciplined programming, to some given data. This allows us to fit a function arising in a convex optimization formulation directly to observed or simulated data. We demonstrate our open-source implementation on several examples, ranging from illustrative to practical. |
| title | Learning Parametric Convex Functions |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2506.04183 |