5 papers · 1 filter
Rethinking Learnability in Offline Data-driven Optimization
Chao Qian, Chen-Guang Wang, Rong-Xi Tan +1
Black-Box Optimization (BBO) has broad applications, while traditional algorithms such as evolutionary algorithms and Bayesian optimization face efficiency challenges as real-world…
Learning Early-to-Final Solution Consistency for MILP Acceleration
Guanlin Li, Chengrui Gao, Chenguang Wang +6
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.…
GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
Yidong Luo, Xuemin Chen, Chenguang Wang +3
Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptimal settings can degrade solv…
A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
Chenguang Wang, Tianshu Yu
We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. For a broad class of standard…
Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
Chenguang Wang, Zihan Zhou, Lei Bai +1
Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries…