Inner-product-induced norm

On this page
  1. Mathematical statement
  2. Representative formula (exact TeX)
  3. Assumptions
  4. Symbol scope
  5. Intuition, definition, and misconceptions
  6. Adopted derivation guide
  7. Prerequisites
  8. Verification boundary
  9. Next obligation
  10. Same-source structured data

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 norm
  • J — 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 ℝ²

The original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.

Prerequisites

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