Parametric Nonconvex Optimization via Convex Surrogates

Renzi Wang; Panagiotis Patrinos; Alberto Bemporad

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arXiv · 2604.05640

Parametric Nonconvex Optimization via Convex Surrogates

Renzi Wang·Panagiotis Patrinos·Alberto Bemporad

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Abstract

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.

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Renzi Wang, Panagiotis Patrinos, Alberto Bemporad. 2026-04-07. Parametric Nonconvex Optimization via Convex Surrogates. https://arxiv.org/abs/2604.05640

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