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Eigenvalues and eigenvectors
The characteristic polynomial and the directions a matrix only stretches.
An eigenvector is a direction a matrix does not rotate, only stretches by its eigenvalue. Find eigenvalues as roots of det(A − λI), then eigenvectors by solving (A − λI)v = 0. In the 3D view the eigenvectors are drawn on the transformed cube.
Przykład pracownika · eigenvalues of [[2,1],[1,2]]
Eigenvalues of [[2,1],[1,2]]
Krok po kroku
- \det(A - \lambda I) = 0
Eigenvalues are the roots of the characteristic polynomial.
- \det\left[\begin{matrix}2 - \lambda & 1\\1 & 2 - \lambda\end{matrix}\right] = 0
Subtract λ from the diagonal.
- \lambda^{2} - 4 \lambda + 3 = 0
Expand the determinant.
- \left(\lambda - 3\right) \left(\lambda - 1\right) = 0
Factor.
- \lambda = 3, \lambda = 1
Eigenvalues (with multiplicity).
- \lambda = 1:\ (A - \lambda I)\mathbf{v} = 0 \Rightarrow \mathbf{v} = \left[\begin{matrix}-1\\1\end{matrix}\right]
Solve (A − 1I)v = 0 for a basis eigenvector.
- \lambda = 3:\ (A - \lambda I)\mathbf{v} = 0 \Rightarrow \mathbf{v} = \left[\begin{matrix}1\\1\end{matrix}\right]
Solve (A − 3I)v = 0 for a basis eigenvector.
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Jak to zrobić?: Eigenvalues and eigenvectors
- Eigenvalues are the roots of the characteristic polynomial.
- Subtract λ from the diagonal.
- Expand the determinant.
- Factor.
- Eigenvalues (with multiplicity).
- Solve (A − 1I)v = 0 for a basis eigenvector.
- Solve (A − 3I)v = 0 for a basis eigenvector.
Pytania, które ludzie zadają
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.
Więcej w Linear Algebra
DeterminantsMatrix inverseMatrix multiplicationRow reductionVector spaces, span and linear independenceOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis