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20112026
most citedAn Efficient Inexact Symmetric Gauss-Seidel Based Majorized ADMM for High-Dimensional Convex Composite Conic Programming

109 citations · 579 across the 90 of their papers we have counts for

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92 papers

math.OC2026

Extension on the convergence rates of moment-SOS hierarchies via approximation of truncated moment sequences

Hoang Anh Tran, Kim-Chuan Toh

This paper continues our work on the convergence rates of moment-SOS hierarchies via approximation of truncated moment sequences. We extend the method developed for the Schmüdgen-t…

math.OC2025

On the B-subdifferential of proximal operators of affine-constrained regularizer

Xudong Li, Meixia Lin, Kim-Chuan Toh

In this work, we study the affine-constrained regularizers, which frequently arise in statistical and machine learning problems across a variety of applications, including…

math.OC2025

On the convergence rates of moment-SOS hierarchies approximation of truncated moment sequences

Hoang Anh Tran, Toh Kim-Chuan

The moment-SOS hierarchy is a widely applicable framework to address polynomial optimization problems over basic semi-algebraic sets based on positivity certificates of polynomial.…

math.OC2024

NewVEM: A Newton Vertex Exchange Method for a Class of Constrained Self-Concordant Minimization Problems

Ling Liang, Kim-Chuan Toh, Haizhao Yang

We propose \textbf{NewVEM}, a Newton vertex exchange method for efficiently solving self-concordant minimization problems under generalized simplex constraints. The algorithm featu…

math.OC2024

Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems

Ling Liang, Qiyuan Pang, Kim-Chuan Toh +1

Solving symmetric positive semidefinite linear systems is an essential task in many scientific computing problems. While Jacobi-type methods, including the classical Jacobi method…

math.OC2024

Wasserstein distributionally robust optimization and its tractable regularization formulations

Hong T. M. Chu, Meixia Lin, Kim-Chuan Toh

We study a variety of Wasserstein distributionally robust optimization (WDRO) problems where the distributions in the ambiguity set are chosen by constraining their Wasserstein dis…