Eigenvalues are a unique collection of scalar values connected to the set of linear equations that are most often found in the matrix equation.
Eigenvalues are the unique set of scalar values connected to the set of linear equations most likely found in the matrix equations. Also known as characteristic roots, the eigenvectors are. After applying linear transformations, the vector is non-zero and can only be altered by its scalar factor.
The term "eigenvalue equation" refers to an equation in which the operator multiplies the function by a constant when applied to a function. An eigenvalue is the resultant numerical value, and an eigenfunction is the function itself.
Given:
The given value is
[tex]$A=\left[\begin{array}{lll}0 & 1 & 0 \\0 & 0 & 1 \\a & b & c\end{array}\right]$[/tex]
where a, b, c are arbitrary constants.
If [tex]$\lambda$[/tex] is an eigen value of A Then,
[tex]${data-answer}amp; E_\lambda={ker}(A-\lambda I) \\[/tex]
Now,
Last two rows are linearly independent.
So, we have
[tex]${dim} E_\lambda=1$[/tex]
Therefore,
[tex]${gemu}(\lambda)=1$[/tex]
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