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cs.LG2026
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Ke Zhao, Zixiang Di, Hong Qian +9
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented…
cs.LG2024
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
Bingdong Li, Zixiang Di, Yongfan Lu +5
Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling compl…
cs.LG2024
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Yongfan Lu, Zixiang Di, Bingdong Li +5
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to tran…