"Efficient Automated Mathematical Optimization with Supervisor-Guided Agentic SLMs: Base, CoT, and Agentic Datasets "
"This dataset supports research on automated mathematical optimization using specialized small language models (SLMs). It consists of 12 JSONL files, each containing 500 records, organized across four task-specific components: decision variables, objective functions, constraints, and CPLEX code generation. Each record follows an instruction\u2013input\u2013output structure, where the input contains a natural-language optimization problem and the output provides the corresponding task-specific formulation or executable solver code. Three dataset variants are provided for each task: Base, Chain-of-Thought (CoT), and Agentic. The Base variant uses direct task instructions, while the CoT and Agentic variants use structured reasoning-oriented instructions to guide task decomposition and formulation. The datasets are designed to support the training and evaluation of specialized language models for translating natural-language optimization problems into structured mathematical formulations and executable CPLEX models."