eigenvalue

As nouns the difference between eigenvalue and eigentime

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigentime is (physics) a time whose value is that of a corresponding eigenvalue.

As nouns the difference between eigenvalue and eigenmass

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigenmass is (physics) a mass whose value is that of a corresponding eigenvalue.

As nouns the difference between eigenvalue and eigensolution

is that eigenvalue is a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the of $x$ under a given linear operator $\backslash rm\; A\backslash !$ is equal to the of $x$ under multiplication by $\backslash lambda$; i.e. $\{\backslash rm\; A\}\; x\; =\; \backslash lambda\; x\backslash !$eigensolution is any of the results of the calculation of eigenvalues.

As nouns the difference between eigenvalue and eigenspectrum

is that eigenvalue is a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the of $x$ under a given linear operator $\backslash rm\; A\backslash !$ is equal to the of $x$ under multiplication by $\backslash lambda$; i.e. $\{\backslash rm\; A\}\; x\; =\; \backslash lambda\; x\backslash !$eigenspectrum is a spectrum of eigenvalues.

As nouns the difference between eigenvalue and eigenstructure

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigenstructure is (mathematics) the set of eigenvalues of a matrix.

In linear algebra|lang=en terms the difference between eigenvalue and eigendecomposition

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigendecomposition is (linear algebra) the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors.

As nouns the difference between eigenvalue and eigendecomposition

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigendecomposition is (linear algebra) the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors.

As nouns the difference between eigenvalue and eigenproblem

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigenproblem is (mathematics) a mathematical problem involving eigenvalues.

In linear algebra|lang=en terms the difference between eigenvalue and eigenspace

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigenspace is (linear algebra) a set of the eigenvectors associated with a particular eigenvalue, together with the zero vector.

As nouns the difference between eigenvalue and eigenspace

is that eigenvalue is (linear algebra) a scalar, $\backslash lambda\backslash !$, such that there exists a vector $x$ (the corresponding eigenvector) for which the image of $x$ under a given linear operator $\backslash rm\; a\backslash !$ is equal to the image of $x$ under multiplication by $\backslash lambda$; ie $\{\backslash rm\; a\}\; x\; =\; \backslash lambda\; x\backslash !$ while eigenspace is (linear algebra) a set of the eigenvectors associated with a particular eigenvalue, together with the zero vector.