Solvercompatible

Solvercompatible is a term used in computing to describe software, data formats, and interfaces that are readily usable by mathematical or logical solvers without requiring substantial transformation. In practice, a model, dataset, or API is solvercompatible if it presents information in a clear, well-defined representation that solvers can parse and process directly. Examples include constraint models expressed in standard modeling languages, problem instances encoded in common formats such as DIMACS for SAT or MPS/LP for linear programming, and APIs that expose explicit objective functions, variables, and constraints.

Key characteristics of solvercompatible artifacts include explicit syntax and semantics, deterministic behavior, and stable numerical representations to avoid ambiguity across solver implementations. They typically support reproducible results, enable validation and testing, and minimize side effects during solving. Transformation-free or minimal-transformation inputs reduce the risk of model misinterpretation and can improve solver performance by reducing preprocessing variance.

The concept is applied in operations research, automated reasoning, verification, and scheduling where interoperability between modeling tools and solvers is important. While not a formal standard, solvercompatible is a design goal that emphasizes interoperability, portability, and efficiency. Related ideas include solver-agnostic modeling languages and standard problem formats such as DIMACS, MPS, and CNF.