English

Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions

Optimization and Control 2019-12-19 v1 Machine Learning Machine Learning

Abstract

In this paper, we propose two discontinuous dynamical systems in continuous time with guaranteed prescribed finite-time local convergence to strict local minima of a given cost function. Our approach consists of exploiting a Lyapunov-based differential inequality for differential inclusions, which leads to finite-time stability and thus finite-time convergence with a provable bound on the settling time. In particular, for exact solutions to the aforementioned differential inequality, the settling-time bound is also exact, thus achieving prescribed finite-time convergence. We thus construct a class of discontinuous dynamical systems, of second order with respect to the cost function, that serve as continuous-time optimization algorithms with finite-time convergence and prescribed convergence time. Finally, we illustrate our results on the Rosenbrock function.

Keywords

Cite

@article{arxiv.1912.08342,
  title  = {Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions},
  author = {Orlando Romero and Mouhacine Benosman},
  journal= {arXiv preprint arXiv:1912.08342},
  year   = {2019}
}

Comments

Presented at workshop "Beyond First Order Methods in Machine Learning" of NeurIPS 2019

R2 v1 2026-06-23T12:49:10.932Z