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Explain the Lean theorem `FApply_sub` 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.

Big AI job. Grok 4.3 reads the canon and writes a Lean-grounded derivation; usually 20 seconds to 2 minutes. Your answer will appear below.
confidence: high in recognition cached

Explanation of the theorem FApply_sub

(1) In plain English, the declaration states that the almost-product operator FApply (defined as F = 2P - I from the normalized projector P) respects vector subtraction: applying FApply to the difference of two vectors equals the difference of the individual applications. This is a basic linearity property.

(2) In Recognition Science this matters because the module IndisputableMonolith.Cost.Ndim.Projector builds the finite-dimensional operator algebra for the rank-one tensor picture underlying cost-induced projectors; linearity under subtraction ensures consistent handling of distinctions and vector operations in the recognition-cost framework.

(3) The formal statement is read as: for any dimension n, scalar lam, inverse metric kernel hInv, vector β, and vectors v, w, we have FApply lam hInv β (v - w) = FApply lam hInv β v - FApply lam hInv β w. The proof proceeds by extensionality (ext i) followed by simplification using the definitions of subtraction as addition of negation.

(4) Visible dependencies in the supplied source are the theorems FApply_add and FApply_neg, which are invoked directly in the simp tactic of the proof. No sorry or external certificates appear; the proof is self-contained within the module.

(5) The declaration does not prove idempotence of the projector, the square law FApply_square, or any connection to the forcing chain, constants, or physical emergence results elsewhere in the canon.

cited recognition theorems

outside recognition

Aspects Recognition does not yet address:

  • Any explicit link from this algebraic identity to the Universal Forcing chain or physical constants
  • Broader Recognition Science interpretation of the operator beyond the module's rank-one tensor context

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.