Eigenvector: Difference between revisions
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= Eigenspace = | = Eigenspace = | ||
Given A and <math>\lambda</math>, the '''eigenspace''' is all the eigenvectors | Given A and <math>\lambda</math>, the '''eigenspace''' is all the eigenvectors. | ||
Because any linear combination of eigenvectors will yield another eigenvector of the same eigenvalue, the eigenspace is a vector space. | Because any linear combination of eigenvectors will yield another eigenvector of the same eigenvalue, the eigenspace is a vector space. | ||
Revision as of 06:43, 10 June 2024
Given a matrix, its eigenvectors are special vectors that satisfy the following property:
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A\vec{x}=\lambda\vec{x} }
where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lambda} is the eigenvalue associated with the eigenvector Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \vec{x}}
The definition of eigenvectors are also frequently written in this form:
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (A-\lambda I)\vec{x}=0 }
Intuition
If we think of a matrix as a linear transformation, eigenvectors do not change direction. Instead, they simply scale by an eigenvalue.
Eigenspace
Given A and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lambda} , the eigenspace is all the eigenvectors.
Because any linear combination of eigenvectors will yield another eigenvector of the same eigenvalue, the eigenspace is a vector space.
The following definition of eigenvectors help explore the eigenspace:
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (A-\lambda I)\vec{x}=0 }
We can find the determinant of the preceding matrix:
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle det(A-\lambda I)=(-1)^n[\lambda^n+c_{n-1}\lambda^{n-1}...+c_0] }
This is the characteristic polynomial of the A, showing that for any A, there are at most n eigenvalues.
