A fundamental problem in combinatorial optimization is identifying equivalent formulations. Despite the growing need for automated equivalence checks---driven, for example, by optimization copilots, which generate problem formulations from natural language descriptions---current approaches rely on simple heuristics that fail to reliably check formulation equivalence.Inspired by Karp reductions, in this workwe introduce Quasi-Karp equivalence, a formal criterion for determining when two optimization formulations are equivalentbased on the existence of a mapping between their decision variables. We propose EquivaMap, a framework that leverages large language models to automatically discover such mappings for scalable, reliable equivalence checking, with a verification stage that ensures mapped solutions preserve feasibility and optimality without additional solver calls. To evaluate our approach, we construct EquivaFormulation, the first open-source dataset of equivalent optimization formulations, generatedby applying transformations such as adding slack variables or valid inequalitiesto existing formulations.Empirically, EquivaMapsignificantly outperforms existing methods, achieving substantial improvements in correctly identifying formulation equivalence.