Solveragnostic

Solveragnostic describes software, interfaces, or problem representations that are not tied to a single solver. A solver-agnostic approach allows a given model or workflow to be solved by multiple underlying engines, enabling benchmarking, portability, and flexibility to switch solvers without rewriting the specification. The term is commonly used in optimization, constraint programming, and related fields.

In practice, solver-agnosticity is achieved through abstraction layers that separate problem formulation from solver implementation. Modeling languages and APIs provide a uniform syntax for defining variables, constraints, and objectives while delegating the actual solving to back-end solvers. Examples include Pyomo and AMPL, which can connect to several solvers such as CBC, GLPK, CPLEX, and Gurobi, and MiniZinc, which targets a range of back-end solvers through a single model.

Benefits of solver-agnostic approaches include greater portability of models across platforms, easier comparative testing of solver capabilities, and reduced vendor lock-in. They also support experimentation with different solvers to identify the best performance for a given problem size or structure.

Drawbacks can include abstraction overhead, potential loss of solver-specific features or optimizations, and constraints on expressing highly specialized constructs. In some cases, the common denominator across solvers may limit modeling expressiveness or require workarounds.

See also: solver-specific, optimization modeling language, solver back-end, MiniZinc, Pyomo, AMPL.