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Explain the Lean theorem `FApply_GApply` in module `IndisputableMonolith.Cost.Ndim.Projector`. 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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1. What the declaration says in plain English The theorem FApply_GApply establishes a linear algebraic identity between two operators: the almost-product operator $F$ and the golden operator $G$. It states that applying $F$ to the output of $G(v)$ is exactly equal to scaling $F(v)$ by $1/2$ and adding the original vector $v$ scaled by $\sqrt{5}/2$. This holds as long as the underlying rank-one operator has a non-zero trace/coefficient ($\mu \neq 0$).

2. Why it matters in Recognition Science In Recognition Science (RS), the golden ratio $\phi$ is structurally forced by self-similarity, rather than being an empirical parameter. In the multi-dimensional setting, the framework formalizes a "rank-one tensor picture" driven by an inverse metric and a covector cost gradient. From this, an idempotent projector $P$ and an involution almost-product operator $F$ ($F^2 = I$) naturally arise. The golden operator $G$ embeds the mathematics of the golden ratio into this spatial geometry. This theorem is a critical computational step used immediately after to prove GApply_square ($G^2 = G + I$), ensuring the spatial operator correctly mimics the algebraic defining polynomial of $\phi$.

3. How to read the formal statement

  • {n : ℕ}: The dimension of the vector space.
  • lam : ℝ, hInv : Fin n → Fin n → ℝ, β : Vec n: The scaling, inverse metric, and covector defining the rank-one geometric update.
  • hμ : mu lam hInv β ≠ 0: The hypothesis that the scalar trace-like coefficient $\mu$ is non-zero, ensuring the projector is well-defined.
  • v : Vec n: An arbitrary vector being acted upon.
  • FApply lam hInv β (GApply lam hInv β v) = ...: The conclusion evaluating the composition $F(G(v)) = \frac{1}{2} F(v) + \frac{\sqrt{5}}{2} v$.

4. Visible dependencies in the supplied source This THEOREM relies entirely on basic linear algebra formalized in the same file:

  • It unfolds GApply, which is defined as $G(v) = \frac{1}{2}v + \frac{\sqrt{5}}{2}F(v)$.
  • It leverages the linearity of $F$ via FApply_add and FApply_smul.
  • It utilizes the involution property of $F$, proved in FApply_square, which states that applying $F$ twice yields the original vector (this is the only step requiring the assumption).

5. What this declaration does not prove

  • It is a purely algebraic theorem and does not prove the root Universal Forcing of $\phi$ from logic (which happens in t6_holds / phi_forcing_complete, not present in this slice).
  • It works generically for any dimension $n$, and thus does not prove that spatial dimension $D = 3$ is forced.
  • It does not directly derive physical constants like $G$ or $c$.

cited recognition theorems

outside recognition

Aspects Recognition does not yet address:

  • The root derivation of phi forcing from logic (t6_holds / phi_forcing_complete) is not in this specific 8-module slice.
  • The specific spatial dimension forcing (t8_holds) is not part of this module's scope.

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.