用凸函数构造非凸优化的近似问题，可并行求解。
Parametric Nonconvex Optimization via Convex Surrogates
- 用凸单调函数的最小值构造代理问题
- 在路径跟踪任务中验证了近似效果良好
- 适合需高效求解非凸问题的研究者
本文提出一种基于学习的新方法，构建一个逼近给定参数化非凸优化问题的代理问题。该代理函数设计为有限个函数的最小值，这些函数由凸函数与单调函数的复合构成，因此代理问题可直接通过并行凸优化求解。作为概念验证，针对非凸路径跟踪问题的数值实验证实了该方法的逼近质量。
原文摘要 · Abstract (English)
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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