maths.free › Linear Algebra › Determinants

Determinants

2×2 by ad − bc, larger by cofactor expansion, and what the number means.

The determinant of a matrix is the factor by which it scales area (2×2) or volume (3×3), with a sign for orientation. A zero determinant means the matrix squashes space flat and cannot be inverted. The 3D view draws the column vectors and the box they span.

Pracovný príklad · det [[1,2],[3,4]]

Linear-Algebra · Determinants

Det [[1,2],[3,4]]

\left[\begin{matrix}1 & 2\\3 & 4\end{matrix}\right]

Krok za krokom

  1. \det\left[\begin{matrix}1 & 2\\3 & 4\end{matrix}\right]

    Determinant of a 2×2 matrix.

  2. = (1)(4) - (2)(3)

    For 2×2: ad − bc.

  3. \det A = -2

    Result.

Odhaliť odpoveď
\det A = -2

Teraz ty Vyberte si problém, alebo napíšte alebo nakreslite vlastný.Každý krok, obrázok, odpoveď skrytá, kým sa neopýtate.

Udržujte svoje vlastné pracovné

Bezplatný účet pridáva poznámky ku každej lekcii, záznam o tom, čo ste dokončili, vaše vyriešené problémy na jednom mieste, a učiteľa, ktorého sa môžete opýtať na túto stránku.Math samotná je otvorená pre každého, prihlásený alebo nie.

Zaregistrovať sa Prihlásenie

Symboly použité tu

Ťuknutím na ľubovoľný symbol zobrazíte úplnú definíciu, obrázok a význam každého písmena.

Ako nakupovať: Determinants

  1. Determinant of a 2×2 matrix.
  2. For 2×2: ad − bc.
  3. Result.

Otázky, ktoré ľudia kladú

What does a determinant mean geometrically?

It is the factor by which the matrix scales area (2×2) or volume (3×3), with a negative sign if orientation flips. Zero means the matrix flattens space and cannot be undone.

What is an eigenvector?

A direction the matrix does not turn: it only stretches it by the eigenvalue. Along eigenvectors a complicated matrix acts like multiplication by a number.

Why is matrix multiplication not commutative?

Because a matrix is a transformation and AB means "do B, then A". Rotating then reflecting is not the same as reflecting then rotating.

Viac v kategórii Linear Algebra

Matrix inverseEigenvalues and eigenvectorsMatrix multiplicationRow reductionVector spaces, span and linear independenceOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis