maths.free › Linear Algebra › Matrix inverse
Matrix inverse
The 2×2 formula and Gauss-Jordan elimination for the rest.
A⁻¹ is the matrix that undoes A. For 2×2 there is a formula; for anything bigger, row-reduce [A | I] until A becomes I, and the identity becomes A⁻¹. Every row operation is shown.
Àwọn ààtò àwọn ìṣàmúlò-ètò · inverse of [[1,2],[3,4]]
Inverse of [[1,2],[3,4]]
Àwọn ìṣàmúlò-ètò
- \det A = -2
A matrix is invertible only when its determinant is non-zero.
- A^{-1} = \frac{1}{\det A}\begin{pmatrix} d & -b \\ -c & a \end{pmatrix} = \frac{1}{-2}\left[\begin{matrix}4 & -2\\-3 & 1\end{matrix}\right]
For 2×2: swap the diagonal, negate the off-diagonal, divide by the determinant.
- A^{-1} = \left[\begin{matrix}-2 & 1\\\frac{3}{2} & - \frac{1}{2}\end{matrix}\right]
Fi àwọn àgbèwọlé hàn
Nígbà yìí Yan àwọn àkóónú kan, tàbí tẹ̀ àtí kọ́ àwọn àkóónú rẹ̀. Àwọn ìṣàmúlò-ètò gbogbo, àwòrán, àwọn à
A free account adds notes on every lesson, a record of what you have finished, your solved problems in one place, and a tutor you can ask about this page. The maths itself is open to everyone, signed in or not.
Fikún Àwọn Ìṣàmúlò-ètòÀwọn àmì-ìwé tí a lo níbẹ̀
Tẹ àwọn àmì-ìwé fún ìṣàfarawé fún àwọn ìṣàmúlò-ètò, àwòrán, àti àwọn àmì-ìwé àwọn àmì-ìwé.
Bii o ṣe le: Matrix inverse
- A matrix is invertible only when its determinant is non-zero.
- For 2×2: swap the diagonal, negate the off-diagonal, divide by the determinant.
Àwọn Àtòjọ-ẹ̀yàn
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.
Diẹ̀ nínú Linear Algebra
DeterminantsEigenvalues and eigenvectorsMatrix multiplicationRow reductionVector spaces, span and linear independenceOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis