7 papers
PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport
Di Wu, Ling Liang, Haizhao Yang
Optimal transport (OT) is a widely used tool in machine learning, but computing high-accuracy solutions for large instances remains costly. Entropic regularization and the Sinkhorn…
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
Di Wu, Ling Liang, Haizhao Yang
Bayesian Optimal Experimental Design (BOED) provides a rigorous framework for decision-making tasks in which data acquisition is often the critical bottleneck, especially in resour…
OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents
Raghav Thind, Youran Sun, Ling Liang +1
Optimization plays a vital role in scientific research and practical applications. However, formulating a concrete optimization problem described in natural language into a mathema…
From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs
Rohan Bhatnagar, Ling Liang, Krish Patel +1
Motivated by the remarkable success of artificial intelligence (AI) across diverse fields, the application of AI to solve scientific problems, often formulated as partial different…
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…
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…