Parametric Nonconvex Optimization via Convex Surrogates
Optimization and Control
2026-04-08 v1 Machine Learning
Systems and Control
Systems and Control
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.
Cite
@article{arxiv.2604.05640,
title = {Parametric Nonconvex Optimization via Convex Surrogates},
author = {Renzi Wang and Panagiotis Patrinos and Alberto Bemporad},
journal= {arXiv preprint arXiv:2604.05640},
year = {2026}
}