Parametric Nonconvex Optimization via Convex Surrogates
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913010091556864 |
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| author | Wang, Renzi Patrinos, Panagiotis Bemporad, Alberto |
| author_facet | Wang, Renzi Patrinos, Panagiotis Bemporad, Alberto |
| contents | This paper presents a novel learning-based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is designed to be the minimum of a finite set of functions, given by the composition of convex and monotonic terms, so that the surrogate problem can be solved directly through parallel convex optimization. As a proof of concept, numerical experiments on a nonconvex path tracking problem confirm the approximation quality of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05640 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Parametric Nonconvex Optimization via Convex Surrogates Wang, Renzi Patrinos, Panagiotis Bemporad, Alberto Optimization and Control Machine Learning Systems and Control This paper presents a novel learning-based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is designed to be the minimum of a finite set of functions, given by the composition of convex and monotonic terms, so that the surrogate problem can be solved directly through parallel convex optimization. As a proof of concept, numerical experiments on a nonconvex path tracking problem confirm the approximation quality of the proposed method. |
| title | Parametric Nonconvex Optimization via Convex Surrogates |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2604.05640 |