Parametric Nonconvex Optimization via Convex Surrogates
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.
| Authors | Renzi Wang, Panagiotis Patrinos, Alberto Bemporad |
|---|---|
| Primary category | math.OC |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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