20 citations · 30 across the 11 of their papers we have counts for
12 papers
ADMM Fails to Achieve an Ergodic KKT Residual Bound
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
The Karush--Kuhn--Tucker (KKT) residual is a fundamental measure of first-order optimality and, under an error bound condition, is comparable to the distance to the KKT solution se…
DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
Maojun Sun, Yue Wu, Yifei Xie +5
Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistica…
DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems
Maojun Sun, Yifei Xie, Yue Wu +5
Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, whi…
dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan +1
This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smoot…
On the Relationships among GPU-Accelerated First-Order Methods for Solving Linear Programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
This paper aims to understand the relationships among recently developed GPU-accelerated first-order methods (FOMs) for linear programming (LP), with particular emphasis on HPR-LP…
HPR-QP: A dual Halpern Peaceman-Rachford method for solving large-scale convex composite quadratic programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
In this paper, we introduce HPR-QP, a dual Halpern Peaceman-Rachford (HPR) method designed for solving large-scale convex composite quadratic programming. One distinctive feature o…
Peaceman-Rachford Splitting Method Converges Ergodically for Solving Convex Optimization Problems
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
In this paper, we prove that the ergodic sequence generated by the Peaceman-Rachford (PR) splitting method with semi-proximal terms converges for convex optimization problems (COPs…
A Survey on Large Language Model-based Agents for Statistics and Data Science
Maojun Sun, Ruijian Han, Binyan Jiang +4
In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis pa…
HPR-LP: An implementation of an HPR method for solving linear programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
In this paper, we introduce an HPR-LP solver, an implementation of a Halpern Peaceman-Rachford (HPR) method with semi-proximal terms for solving linear programming (LP). The HPR me…
HOT: An Efficient Halpern Accelerating Algorithm for Optimal Transport Problems
Guojun Zhang, Zhexuan Gu, Yancheng Yuan +1
This paper proposes an efficient HOT algorithm for solving the optimal transport (OT) problems with finite supports. We particularly focus on an efficient implementation of the HOT…
LAMBDA: A Large Model Based Data Agent
Maojun Sun, Ruijian Han, Binyan Jiang +4
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is des…
Accelerating preconditioned ADMM via degenerate proximal point mappings
Defeng Sun, Yancheng Yuan, Guojun Zhang +1
In this paper, we aim to accelerate a preconditioned alternating direction method of multipliers (pADMM), whose proximal terms are convex quadratic functions, for solving linearly…