mlsanityData to 7 Oct 2026

Parametric Nonconvex Optimization via Convex Surrogates

arXiv 2604.05640 ↗First posted 7 Apr 2026math.OC cs.LG cs.SYcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

22 of 35 references.

References · 22

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10 more
  1. 2023Learning to Warm-Start Fixed-Point Optimization Algorithmscited by 32
  2. 2021Accelerating Quadratic Optimization with Reinforcement Learningcited by 30
  3. 2023RAYEN: Imposition of Hard Convex Constraints on Neural Networkscited by 30
  4. 2020Automatically Learning Compact Quality-aware Surrogates for Optimization Problemscited by 22
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  8. 2025Learning Parametric Convex Functionscited by 2
  9. 2024Latent Linear Quadratic Regulator for Robotic Control Taskscited by 1
  10. 2026Koopman-BoxQP: Solving Large-Scale NMPC at kHz Ratescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functionscited by 1