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Autores principales: Schaller, Maximilian, Bemporad, Alberto, Boyd, Stephen
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2506.04183
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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