Linear map between real vector spaces

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 map between real vector spaces is linear when it preserves linear combinations for all vectors and real scalars.

mko-general-linear-map · definition · v0.10.0

Mathematical statement

A real linear map T:V→W satisfies T(alpha*u+beta*v)=alpha*T(u)+beta*T(v) for all u,v and real alpha,beta. A finite-dimensional matrix formula supplies a universal coordinate certificate; a nonzero affine translation fails linearity.

Representative formula (exact TeX)

T(v)

Assumptions

  • Exercises explicitly give a matrix or matrix-plus-bias formula from R^n to R^m.
  • Matrix distributivity supplies the universal certificate; finitely many sampled inputs do not prove an arbitrary black-box map linear.

Symbol scope

  • T — real linear map
  • V — source space
  • W — target space

Intuition, definition, and misconceptions

One identity encompasses both additivity and real homogeneity.

Matrix multiplication gives a linear map in finite-dimensional coordinates.

The affine formula Ax+b qualifies as linear exactly when b=0.

An arbitrary affine map, a black box passing only a few samples, or an additive function without real homogeneity need not be real-linear.

Adopted derivation guide

Linear maps between real vector spaces: a universal certificate for a matrix map, with affine and black-box counterexamples

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

Prerequisites

Verification boundary

The coordinate-matrix certificate covers only the explicit finite-dimensional formula; it does not certify arbitrary black boxes or make separate boundedness, continuity, injectivity or surjectivity claims.

not_formalized

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

Next obligation

Add bounded linear operators on normed 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