15 papers
OptGraph: Large Language Models Enhanced Evolutionary Optimization Via Graph Retrieval-Augmented Generation
Xianchao Xiu, Jianhao Li, Huangyue Chen +1
The paper introduces OptGraph, a system that enhances automated evolutionary optimization by using large language models together with a graph‑based retrieval‑augmented generation…
Vehicle Routing Problem Meets Large Language Models: An Overview and Perspectives
Xianchao Xiu, Chong Shen, Yanjiao Zhu +1
The vehicle routing problem (VRP) is a central optimization problem in artificial intelligence, logistics automation, transportation scheduling, and industrial decision-making. VRP…
Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing
Hui Chen, Liangyu Liu, Xianchao Xiu +1
Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundances. Despite significant progress…
Large Language Models for Operations Research: A Comprehensive Survey
Xianchao Xiu, Jianhao Li, Jun Fan +1
Operations Research (OR) serves as a core decision-support methodology for complex systems, with significant applications across mathematics, management science, and computer scien…
Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
Pu Qiu, Chen Ouyang, Yongyang Xiong +3
Federated Composite Optimization (FCO) has emerged as a promising framework for training models with structural constraints (e.g., sparsity) in distributed edge networks. However,…
Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection
Xianchao Xiu, Chenyi Huang, Wei Zhang +1
Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence…