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Eigenvectors

Eigenvectors[m]
gives a list of the eigenvectors of the square matrix m.
Eigenvectors
gives the generalized eigenvectors of m with respect to a.
Eigenvectors
gives the first k eigenvectors of m.
Eigenvectors
gives the first k generalized eigenvectors.
MORE INFORMATION
  • Eigenvectors finds numerical eigenvectors if m contains approximate real or complex numbers.
  • For approximate numerical matrices m, the eigenvectors are normalized.
  • Eigenvectors corresponding to degenerate eigenvalues are chosen to be linearly independent.
  • For an nn matrix, Eigenvectors always returns a list of length n. The list contains each of the independent eigenvectors of the matrix, followed if necessary by an appropriate number of vectors of zeros. »
  • Eigenvectors with numeric eigenvalues are sorted in order of decreasing absolute value of their eigenvalues.
  • The option settings Cubics->True and Quartics->True can be used to specify that explicit radicals should be generated for all cubics and quartics.
EXAMPLESCLOSE ALL
Basic Examples   (3)
Symbolic eigenvectors:
Exact eigenvectors:
Numerical value:
Eigenvectors computed using numerical methods:
Symbolic eigenvectors:
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Exact eigenvectors:
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Numerical value:
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Eigenvectors computed using numerical methods:
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Scope   (4)
Eigenvectors computed using numerical methods with 20-digit precision:
A Hilbert matrix:
Eigenvectors corresponding to the eigenvalues with smallest magnitude:
The corresponding eigenvalues:
Zero vectors are used when there are more eigenvalues than independent eigenvectors:
Compute generalized eigenvectors:
Generalizations & Extensions   (2)
A block diagonal matrix:
Compute the eigenvectors:
A tridiagonal matrix:
The eigenvectors corresponding to the three largest eigenvalues:
Applications   (2)
The eigenvectors of a 3×3 matrix m:
Diagonalize m:
The eigenvalues of a nondiagonalizable matrix:
Find the dimension of the span of all the eigenvectors:
Estimate the probability that a random 4×4 matrix of ones and zeros is not diagonalizable:
Properties & Relations   (2)
Compute the eigenvectors for a random symmetric matrix:
The numerical eigenvectors are orthonormal to the precision of the computation:
Diagonalization of the matrix r:
The diagonal elements are essentially the same as the eigenvalues:
The first eigenvector of a random matrix:
The position of the largest component in v:
Compute the eigenvalue corresponding to eigenvector v:
Possible Issues   (3)
The general symbolic case quickly gets very complicated:
The expression sizes increase faster than exponentially:
Construct a 10,000×10,000 sparse matrix:
The eigenvector matrix is a dense matrix, and too large to represent:
Computing the few eigenvectors corresponding to the largest eigenvalues is much easier:
When eigenvalues are closely grouped, the iterative method for sparse matrices may not converge:
The iteration has not converged well after 1000 iterations:
You can give the algorithm a shift near an expected eigenvalue to speed up convergence:
Neat Examples   (1)
The first four eigenvectors of a banded matrix:
A plot of the first four eigenvectors:
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