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Vec

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Vec n is the type of n-component real vectors represented as maps from the finite index set Fin n to the reals. Researchers extending the scalar reciprocal cost kernel to multiple dimensions cite this as the basic coordinate space for weighted aggregates. The declaration is a direct type abbreviation with no further structure or proof obligations.

claimFor each natural number $n$, let $V_n$ denote the set of all functions from the finite set of indices with $n$ elements to the real numbers, serving as the coordinate representation of $n$-dimensional vectors.

background

The module Cost.Ndim.Core introduces the multi-component reciprocal cost by lifting the scalar kernel through a weighted log aggregate. Supporting definitions include the weighted dot product, which sums componentwise products of two vectors, and the exponential aggregate, which recovers a scalar from weights and log-coordinates via the dot product. These rest on the one-dimensional J-cost and Recognition Composition Law from the parent Cost module.

proof idea

The declaration is a direct abbreviation of the function type Fin n to the reals, supplying the vector space of coordinates without additional axioms or lemmas.

why it matters in Recognition Science

This vector type supplies the domain for all N-dimensional cost constructions and feeds directly into the Bridge module theorems, including additive_decomposition, compensatory_nonneg_of_sqNorm_le_one, and multiplicative_le_additive_of_sqNorm_le_one. It extends the scalar J-uniqueness (T5) and phi-ladder structure to higher dimensions while preserving the eight-tick octave and D=3 spatial setting of the Recognition framework. No open scaffolding remains at this level.

scope and limits

formal statement (Lean)

  18abbrev Vec (n : ℕ) := Fin n → ℝ

proof body

Definition body.

  19
  20/-- Weighted dot product used by the logarithmic aggregate. -/

used by (40)

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depends on (2)

Lean names referenced from this declaration's body.