base (version 3.0.3)

eigen: Spectral Decomposition of a Matrix

Description

Computes eigenvalues and eigenvectors of numeric (double, integer, logical) or complex matrices.

Usage

eigen(x, symmetric, only.values = FALSE, EISPACK = FALSE)

Arguments

x

a numeric or complex matrix whose spectral decomposition is to be computed. Logical matrices are coerced to numeric.

symmetric

if TRUE, the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle (diagonal included) is used. If symmetric is not specified, the matrix is inspected for symmetry.

only.values

if TRUE, only the eigenvalues are computed and returned, otherwise both eigenvalues and eigenvectors are returned.

EISPACK

logical. Defunct and ignored (with a warning for true values).

Value

The spectral decomposition of x is returned as components of a list with components

values

a vector containing the $p$ eigenvalues of x, sorted in decreasing order, according to Mod(values) in the asymmetric case when they might be complex (even for real matrices). For real asymmetric matrices the vector will be complex only if complex conjugate pairs of eigenvalues are detected.

vectors

either a $p * p$ matrix whose columns contain the eigenvectors of x, or NULL if only.values is TRUE. The vectors are normalized to unit length.Recall that the eigenvectors are only defined up to a constant: even when the length is specified they are still only defined up to a scalar of modulus one (the sign for real matrices).

If r <- eigen(A), and V <- r$vectors; lam <- r$values, then $$A = V \Lambda V^{-1}$$ (up to numerical fuzz), where $Lmbd =$diag(lam).

Source

By default eigen uses the LAPACK routines DSYEVR, DGEEV, ZHEEV and ZGEEV whereas LAPACK is from http://www.netlib.org/lapack and its guide is listed in the references.

Details

If symmetric is unspecified, the code attempts to determine if the matrix is symmetric up to plausible numerical inaccuracies. It is faster and surer to set the value yourself.

Computing the eigenvectors is the slow part for large matrices.

Computing the eigendecomposition of a matrix is subject to errors on a real-world computer: the definitive analysis is Wilkinson (1965). All you can hope for is a solution to a problem suitably close to x. So even though a real asymmetric x may have an algebraic solution with repeated real eigenvalues, the computed solution may be of a similar matrix with complex conjugate pairs of eigenvalues.

References

Anderson. E. and ten others (1999) LAPACK Users' Guide. Third Edition. SIAM. Available on-line at http://www.netlib.org/lapack/lug/lapack_lug.html.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. Springer-Verlag Lecture Notes in Computer Science 6.

Wilkinson, J. H. (1965) The Algebraic Eigenvalue Problem. Clarendon Press, Oxford.

See Also

svd, a generalization of eigen; qr, and chol for related decompositions.

To compute the determinant of a matrix, the qr decomposition is much more efficient: det.

Examples

eigen(cbind(c(1,-1), c(-1,1)))
eigen(cbind(c(1,-1), c(-1,1)), symmetric = FALSE)
# same (different algorithm).

eigen(cbind(1, c(1,-1)), only.values = TRUE)
eigen(cbind(-1, 2:1)) # complex values
eigen(print(cbind(c(0, 1i), c(-1i, 0)))) # Hermite ==> real Eigenvalues
## 3 x 3:
eigen(cbind( 1, 3:1, 1:3))
eigen(cbind(-1, c(1:2,0), 0:2)) # complex values