3 citations · 8 across the 10 of their papers we have counts for
10 papers
A Customized Augmented Lagrangian Method for Block-Structured Integer Programming
Rui Wang, Chuwen Zhang, Shanwen Pu +2
Integer programming with block structures has received considerable attention recently and is widely used in many practical applications such as train timetabling and vehicle routi…
ODE-based Learning to Optimize
Zhonglin Xie, Wotao Yin, Zaiwen Wen
Recent years have seen a growing interest in understanding acceleration methods through the lens of ordinary differential equations (ODEs). Despite the theoretical advancements, tr…
Sharper Convergence Guarantees for Federated Learning with Partial Model Personalization
Yiming Chen, Liyuan Cao, Kun Yuan +1
Partial model personalization, which encompasses both shared and personal variables in its formulation, is a critical optimization problem in federated learning. It balances indivi…
On the Optimal Lower and Upper Complexity Bounds for a Class of Composite Optimization Problems
Zhenyuan Zhu, Fan Chen, Junyu Zhang +1
We study the optimal lower and upper complexity bounds for finding approximate solutions to the composite problem , where is smooth and is convex. Giv…
A Unified Primal-Dual Algorithm Framework for Inequality Constrained Problems
Zhenyuan Zhu, Fan Chen, Junyu Zhang +1
In this paper, we propose a unified primal-dual algorithm framework based on the augmented Lagrangian function for composite convex problems with conic inequality constraints. The…
Riemannian Natural Gradient Methods
Jiang Hu, Ruicheng Ao, Anthony Man-Cho So +2
This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in var…