OptiDSL: A DSL-Centric Framework for Flexible Optimization Modeling
A recent publication on arXiv (2608.07040) contends that not every combinatorial optimization problem (COP) is optimally represented through mixed-integer linear programming (MILP). The authors argue that imposing intricate domains into linear constraints can lead to excessive modeling complexity and limit solver adaptability. They introduce OptiDSL, a framework that transitions from inflexible MILP approaches to domain-specific language (DSL) models. By leveraging large language models (LLMs) to translate natural language into standardized, domain-recognized formats, OptiDSL separates problem formulation from execution, facilitating smooth integration with a wide array of specialized solvers, including both traditional heuristics and contemporary learning-based techniques. The paper was published on arXiv under the identifier 2608.07040.
Key facts
- Paper arXiv:2608.07040 proposes OptiDSL framework.
- OptiDSL uses DSL representations instead of rigid MILP formulations.
- LLMs map natural language to standardized domain structures.
- Framework decouples problem formulation from execution.
- Integrates with diverse solvers: heuristics and learning-based.
- Argues MILP can induce prohibitive modeling complexity.
- Focuses on combinatorial optimization problems (COPs).
Entities
Institutions
- arXiv