4 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
Finite-Time Optimization via Scaled Gradient-Momentum Flows
Yu Zhou, Mengmou Li, Masaaki Nagahara
In this paper, we develop a scaled gradient-momentum framework for continuous-time optimization that achieves global finite-time convergence. A state-dependent scaling mechanism is…
A Canonical Structure for Constructing Projected First-Order Algorithms With Delayed Feedback
Mengmou Li, Yu Zhou, Xun Shen +1
This work introduces a canonical structure for a broad class of unconstrained first-order algorithms that admit a Lur'e representation, including systems with relative degree great…
Small-Gain Theorem Based Distributed Prescribed-Time Convex Optimization For Networked Euler-Lagrange Systems
Gewei Zuo, Mengmou Li, Lijun Zhu
In this paper, we address the distributed prescribed-time convex optimization (DPTCO) for a class of networked Euler-Lagrange systems under undirected connected graphs. By utilizin…
Convergence Rate Bounds for the Mirror Descent Method: IQCs, Popov Criterion and Bregman Divergence
Mengmou Li, Khaled Laib, Takeshi Hatanaka +1
This paper presents a comprehensive convergence analysis for the mirror descent (MD) method, a widely used algorithm in convex optimization. The key feature of this algorithm is th…