Author

Yong Li

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Preprint Aug 2026

Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling

OptiDSL is proposed, a framework that shifts the focus from rigid MILP formulations to domain-specific language (DSL) representations, and enables seamless integration with a diverse library of specialized solvers, ranging from traditional heuristics to modern learning-based methods.

Shaofeng Zhang, Hongyuan Su, Qing Peng et al. · 0 citations

Open access Jul 2026

LLM-MetaAS: A Semantic-Statistical Policy Routing Framework for AutoML Execution Strategy Selection

LLM-MetaAS is proposed, a semantic-statistical framework for AutoML execution policy selection that improves overall AutoML performance and selects policies closer to the oracle than fixed strategies, random selection, and the native Auto-sklearn 2.0 selector.

Zhihuan Peng, Pincheng Liu, Yong Li et al. · Applied Sciences · 0 citations