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