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Explain the Lean lemma `ode_diagonalization` 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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Explanation of ode_diagonalization

(1) Plain English

The lemma states that if a real-valued function f is twice continuously differentiable and obeys the linear ODE f''(t) = f(t) for every real t, then the auxiliary functions g(t) = f'(t) - f(t) and h(t) = f'(t) + f(t) each satisfy a first-order ODE: g'(t) = -g(t) and h'(t) = h(t). This splits the original second-order equation into two independent first-order equations whose solutions are immediately recognizable (exponentials with rates ±1).

(2) Why it matters in Recognition Science

This step belongs to the infrastructure used to establish uniqueness of the J-cost function from its functional equation. By diagonalizing the ODE that arises from the d'Alembert-type identity satisfied by the reparametrized cost, the lemma supplies the differential relations needed to conclude that the only solutions compatible with reciprocity and normalization are of the form cosh(t) - 1, which translates back to the explicit formula J(x) = (x + 1/x)/2 - 1.

(3) How to read the formal statement

lemma ode_diagonalization (f : ℝ → ℝ)
    (h_diff2 : ContDiff ℝ 2 f)
    (h_ode : ∀ t, deriv (deriv f) t = f t) :
    (∀ t, deriv (fun s => deriv f s - f s) t = -(deriv f t - f t)) ∧
    (∀ t, deriv (fun s => deriv f s + f s) t = deriv f t + f t)
  • f : ℝ → ℝ is the unknown function.
  • ContDiff ℝ 2 f asserts f is C^{2} (twice continuously differentiable).
  • h_ode is the hypothesis that the second derivative equals the function itself.
  • The conclusion is a conjunction of two universal statements, each asserting that the derivative of one linear combination equals ± that same combination.

(4) Visible dependencies or certificates in the supplied source

The lemma appears verbatim in IndisputableMonolith.Cost.FunctionalEquation. Its proof invokes ContDiff, deriv, differentiable, and deriv_sub/deriv_add from Mathlib, together with the earlier lemmas dAlembert_double, dAlembert_product and dAlembert_diff_square that live in the same file. No sorry appears; the declaration is fully proved. It is placed immediately after the d'Alembert identities and before the zero-solution lemmas deriv_neg_self_zero and deriv_pos_self_zero.

(5) What this declaration does not prove

It assumes the ODE f'' = f rather than deriving it from the original functional equation; it does not establish that the only global solutions are hyperbolic cosines; it does not address boundary conditions, reciprocity symmetry, or normalization that ultimately select the J-cost. Those steps require the surrounding lemmas in the same module and the broader uniqueness argument for the recognition cost.

cited recognition theorems

outside recognition

Aspects Recognition does not yet address:

  • Full derivation of the ODE from the J-cost functional equation
  • Uniqueness theorem that selects Jcost among all solutions
  • Connection to T5 or the Law of Logic forcing chain

recognition modules consulted

The Recognition library is at github.com/jonwashburn/shape-of-logic. The model is restricted to the supplied Lean source and instructed not to invent theorem names. Treat output as a starting point, not a verified proof.