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Euclidean length

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.

The distance between two points in the plane is defined as the nonnegative square root of the sum of squared coordinate differences.

mko-euclidean-length · definition · v0.5.0

Mathematical statement

In the Euclidean plane, if P=(x₁,y₁) and Q=(x₂,y₂), their distance is defined as the nonnegative square root of the sum of the squared coordinate differences.

Representative formula (exact TeX)

d(P,Q)=\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}

Assumptions

  • P and Q are points in the two-dimensional Euclidean plane.
  • The coordinates x₁, y₁, x₂, and y₂ are real numbers.
  • The square root is the nonnegative principal value, so distance is nonnegative.

Symbol scope

  • d — distance function
  • P — first point in the plane
  • Q — second point in the plane
  • x₁,y₁ — coordinates of P
  • x₂,y₂ — coordinates of Q

Intuition, definition, and misconceptions

The distance formula treats horizontal and vertical displacement as perpendicular components and takes the nonnegative square root of their squared sum.

When P and Q coincide, both coordinate differences are zero, so the distance is zero. Exchanging P and Q does not change the distance.

A program can use hypot or a square root to compute a concrete distance, but floating-point representations are not identical to mathematical real numbers.

A common mistake is to ignore the nonnegative principal square root or to add coordinate differences without squaring them first.

Adopted derivation guide

Euclidean length: computing the distance between two points exactly from coordinates

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

Prerequisites

    Verification boundary

    Concrete coordinate tests validate only the reference implementation; they do not prove that the distance function satisfies every metric axiom for all real points.

    not_formalized

    Next obligation

    Connect the entry to Mathlib EuclideanSpace and dist, and record a theorem relating the two-dimensional coordinate formula to the abstract distance.

    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