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eigenvalues

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Definitions of eigenvalues in various dictionaries:

noun - (mathematics) any number such that a given square matrix minus that number times the identity matrix has a zero determinant

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Plural form of eigenvalue.
Each of a set of values of a parameter for which a differential equation has a non-zero solution (an eigenfunction) under given conditions.
Any number such that a given matrix minus that number times the identity matrix has zero determinant.

Eigenvalues might refer to
In linear algebra, an eigenvector or characteristic vector of a linear transformation is a non-zero vector that changes by only a scalar factor when that linear transformation is applied to it. More formally, if T is a linear transformation from a vector space V over a field F into itself and v is a vector in V that is not the zero vector, then v is an eigenvector of T if T(v) is a scalar multiple of v. This condition can be written as the equation* * * * T * ( * * v * * ) * = * * * v * * , * * * {\displaystyle T(\mathbf {v} )=\lambda \mathbf {v} ,} * where is a scalar in the field F, known as the eigenvalue, characteristic value, or characteristic root associated with the eigenvector v. * If the vector space V is finite-dimensional, then the linear transformation T can be represented as a square matrix A, and the vector v by a column vector, rendering the above mapping as a matrix mu

Eigenvalues might refer to

In linear algebra, an eigenvector or characteristic vector of a linear transformation is a non-zero vector that changes by only a scalar factor when that linear transformation is applied to it. More formally, if T is a linear transformation from a vector space V over a field F into itself and v is a vector in V that is not the zero vector, then v is an eigenvector of T if T(v) is a scalar multiple of v. This condition can be written as the equation*
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* {\displaystyle T(\mathbf {v} )=\lambda \mathbf {v} ,}
* where is a scalar in the field F, known as the eigenvalue, characteristic value, or characteristic root associated with the eigenvector v.
* If the vector space V is finite-dimensional, then the linear transformation T can be represented as a square matrix A, and the vector v by a column vector, rendering the above mapping as a matrix mu

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