The supplied source for module IndisputableMonolith.Cost.FunctionalEquation defines multiple supporting lemmas and theorems on d'Alembert-type functional equations, reparametrizations G and H, cosh identities, continuity from log curvature, and ODE uniqueness infrastructure (including deriv_exp_neg, ode_diagonalization, deriv_neg_self_zero, and deriv_pos_self_zero). However, the specific declaration dAlembert_to_ODE_general_theorem does not appear verbatim anywhere in the provided Lean source. No explanation or formal statement of it can be given from the canon.
Explain the Lean theorem `dAlembert_to_ODE_general_theorem` in module `IndisputableMonolith.Cost.FunctionalEquation`. Write for an educated reader who knows science and programming but may not know Lean. Cover: (1) what the declaration says in plain English, (2) why it matters in Recognition Science, (3) how to read the formal statement, (4) visible dependencies or certificates in the supplied source, and (5) what this declaration does not prove. Cite only declarations present in the supplied Recognition source.
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- dAlembert_to_ODE_general_theorem
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IndisputableMonolith.Foundation.AlexanderDualityIndisputableMonolith.Mathematics.LanglandsFromRecognitionCostIndisputableMonolith.Foundation.RealityFromDistinctionIndisputableMonolith.Measurement.RSNative.Calibration.SingleAnchorIndisputableMonolith.Unification.RecognitionBandGeometryIndisputableMonolith.Unification.RecognitionBandwidthIndisputableMonolith.Cost.AczelClassIndisputableMonolith.Cost.FunctionalEquation