Real vector subspace

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.

A nonempty subset of a real vector space is a subspace when it contains zero and is closed under vector addition and multiplication by every real scalar.

mko-vector-subspace · definition · v0.10.0

Mathematical statement

This slice handles the zero space, the whole space, lines through the origin and explicit finite counterexamples in R². Positive cases use universal algebraic certificates from homogeneous linear equations or spans. Finitely many successful samples do not prove a subspace.

Representative formula (exact TeX)

U(V)

Assumptions

  • The ambient space is fixed as R² with the usual vector operations.
  • Positive certificates cover addition and every real scalar universally; an affine line not through zero is not a subspace.

Symbol scope

  • U — candidate subspace
  • V — ambient real vector space

Intuition, definition, and misconceptions

Containing the zero vector is necessary.

A homogeneous line through the origin has a universal closure certificate from its linear equation.

One counterexample refutes a subspace; finitely many positive samples do not prove it.

An arbitrary affine line, or finitely sampled closure, is not automatically a subspace certificate.

Adopted derivation guide

Universal closure of a line through zero

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

Prerequisites

Verification boundary

Recomputation covers only R² and the explicit certificates, not arbitrary dimensions or unknown operations.

not_formalized

This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.

Next obligation

Add kernels, images, dimension and quotient vector 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