activity
20222026
collaborators

6 papers

math.OC2026

Toward a Systematic Understanding and Interactive Search of Lyapunov-Style Proofs in Optimization

TaeHo Yoon, Jaewook J. Suh, Edward Duc Hien Nguyen +2

Lyapunov-style convergence proofs, which establish a nonincreasing sequence to provide a quantitative convergence rate for an algorithm, are popular and often considered desirable…

math.OC2025

Exact worst-case convergence rates for Douglas--Rachford and Davis--Yin splitting methods

Edward Duc Hien Nguyen, Jaewook J. Suh, Xin Jiang +1

In this work, we aim to establish the exact worst-case convergence rates of Douglas--Rachford splitting (DRS) and Davis--Yin splitting (DYS) when applied to convex optimization pro…

math.OC2024

Sparse factorization of the square all-ones matrix of arbitrary order

Xin Jiang, Edward Duc Hien Nguyen, César A. Uribe +1

In this paper, we study sparse factorization of the (scaled) square all-ones matrix of arbitrary order. We introduce the concept of hierarchically banded matrices and propose t…

math.OC2023

On graphs with finite-time consensus and their use in gradient tracking

Edward Duc Hien Nguyen, Xin Jiang, Bicheng Ying +1

This paper studies sequences of graphs satisfying the finite-time consensus property (i.e., iterating through such a finite sequence is equivalent to performing global or exact ave…

math.OC2022

On the Performance of Gradient Tracking with Local Updates

Edward Duc Hien Nguyen, Sulaiman A. Alghunaim, Kun Yuan +1

We study the decentralized optimization problem where a network of agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State…

math.OC2022

On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression

Ekaterina Trimbach, Edward Duc Hien Nguyen, César A. Uribe

We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization…