Inner-product-induced norm
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This English edition is an AI candidate and does not yet have an OCME Atlas (GPT-6) Twin closure receipt.
On the same real inner-product space, the norm is the nonnegative square root of the self-inner-product; the triangle inequality uses the universal Cauchy–Schwarz certificate.
mko-inner-product-induced-norm · definition · v0.10.0
Mathematical statement
In one real inner-product space define norm(x)=sqrt(<x,x>). Positive definiteness gives separation, homogeneity gives absolute homogeneity, and Cauchy–Schwarz gives the triangle inequality. This slice calculates exact squared norms and identities for positive-definite matrices on R² without replacing universal certificates with vector samples.
Representative formula (exact TeX)
N(J)
Assumptions
- The matrix already qualifies as bilinear, symmetric and positive definite, and the vectors lie in the same R².
- Square roots remain exact symbolic expressions; Cauchy–Schwarz is a universal certificate not established by finitely many vectors.
Symbol scope
N— inner-product-induced normJ— same real inner product
Intuition, definition, and misconceptions
Compute the squared norm <x,x> with exact integer arithmetic first.
The squared distance is <x−y,x−y>.
Cauchy–Schwarz and the parallelogram identity check the structure but do not imply completeness.
A few successful triangle-inequality samples do not certify a general norm, and an inner-product-induced norm alone does not make a space Hilbert.
Adopted derivation guide
Inner-product-induced norm: proving √⟨x,x⟩ is a norm and computing exactly for a positive-definite matrix on ℝ²The original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Real inner-product space — The formula uses the same certified inner product.
- Natural-number powers and principal square roots — The norm is the nonnegative square root of the self-inner-product.
- Usual order on the real numbers — Positive definiteness and Cauchy–Schwarz use the real ordering.
Verification boundary
Only the explicit R² inner-product matrix and exact certificates are checked; completeness and Hilbert structure are not inferred.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Combine with completeness to add real Hilbert spaces.
Same-source structured data
This page and the interactive view use the same object and existing language edition; this adds no proof or human review.
Canonical MKO JSON · Plain Markdown