Significance Many scientific and engineering fields, including optimization, machine learning, and quantum computing, use semidefinite programming (SDP) to tackle complex problems. Unfortunately, as these problems grow, they rapidly exceed the capabilities of conventional SDP solvers, creating a critical bottleneck. By...
Qiu-Shi Han, Zhen-Wei Lin, Han-Wen Liu et al.· Proceedings of the National...· 1 citation
This work shows empirically that solver-integrated reasoning, exact combinatorial algorithm, and heuristic search exhibit complementary strengths across different problem structures and scales, and introduces Strategy-Diverse Reinforcement Learning (SDRL), which trains LLMs as adaptive optimization meta-solvers.
Shi-Hao Zhang, Wei-Ting Liu, Si-Yu Shao et al.· 0 citations
SOLID is proposed, a novel framework for self-improving OR language models without verified answers or external evaluators that improves solution accuracy for both general-purpose and OR-tuned models over outcome-only group-relative training.
Rui-Chen Zhu, Ming-Long Cao, Chen-Yu Zhou et al.· 0 citations
PDHCG-CQP achieves state-of-the-art robustness among first-order solvers while scaling efficiently to 8 GPUs and instances with up to $4.4\times10^8$ stored primal coordinates.
Hongpei Li, Yicheng Huang, Hui-Kang Liu et al.· 3 citations
This work introduces OR-Clarify, a benchmark for pre-formulation clarification and proposes Interactive Optimization (InterOPT), a two-stage framework that identifies unresolved formulation-critical gaps and uses them to guide whether to ask the next question or to stop.
Si-Han Ge, Yi-Chen Lin, Chen-Yu Zhou et al.· 0 citations
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