eigen {base} | R Documentation |
Function eigen
computes eigenvalues and eigenvectors by providing an
interface to the EISPACK routines RS
, RG
, CH
and CG
.
Function La.eigen
uses the LAPACK routines DSYEV/DSYEVR,
DGEEV, ZHEEV and ZGEEV.
eigen(x, symmetric, only.values = FALSE)
La.eigen(x, symmetric, only.values = FALSE,
method = c("dsyevr", "dsyev"))
x |
a matrix whose spectral decomposition is to be computed. |
symmetric |
if |
only.values |
if |
method |
The LAPACK routine to use in the real symmetric case. |
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.
La.eigen
is preferred to eigen
for new projects, but
its eigenvectors may differ in sign and (in the asymmetric case) in
normalization. (They may also differ between methods
and between platforms.)
The LAPACK routine DSYEVR is usually substantially faster than DSYEV: see http://www.cs.berkeley.edu/~demmel/DOE2000/Report0100.html. Most benefits are seen with an optimized BLAS system.
Computing the eigenvectors is the slow part for large matrices.
Using method="dsyevr"
requires IEEE 754 arithmetic. Should
this not be supported on your platform, method="dsyev"
is
used, with a warning.
The spectral decomposition of x
is returned
as components of a list.
values |
a vector containing the |
vectors |
a For 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). |
Smith, B. T, Boyle, J. M., Dongarra, J. J., Garbow, B. S., Ikebe,Y., Klema, V., and Moler, C. B. (1976). Matrix Eigensystems Routines – EISPACK Guide. Springer-Verlag Lecture Notes in Computer Science.
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.
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
.
capabilities
to test for IEEE 754 arithmetic.
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 Eigen values
## 3 x 3:
eigen(cbind( 1,3:1,1:3))
eigen(cbind(-1,c(1:2,0),0:2)) # complex values
Meps <- .Machine$double.eps
set.seed(321, kind = "default") # force a particular seed
m <- matrix(round(rnorm(25),3), 5,5)
sm <- m + t(m) #- symmetric matrix
em <- eigen(sm); V <- em$vect
print(lam <- em$values) # ordered DEcreasingly
stopifnot(
abs(sm %*% V - V %*% diag(lam)) < 60*Meps,
abs(sm - V %*% diag(lam) %*% t(V)) < 60*Meps)
##------- Symmetric = FALSE: -- different to above : ---
em <- eigen(sm, symmetric = FALSE); V2 <- em$vect
print(lam2 <- em$values) # ordered decreasingly in ABSolute value !
# and V2 is not normalized (where V is):
print(i <- rev(order(lam2)))
stopifnot(abs(lam - lam2[i]) < 60 * Meps)
zapsmall(Diag <- t(V2) %*% V2) # orthogonal, but not normalized
print(norm2V <- colSums(V2 * V2))
stopifnot( abs(1- norm2V / diag(Diag)) < 60*Meps)
V2n <- sweep(V2,2, STATS= sqrt(norm2V), FUN="/")## V2n are now Normalized EV
apply(V2n * V2n, 2, sum)
##[1] 1 1 1 1 1
## Both are now TRUE:
stopifnot(abs(sm %*% V2n - V2n %*% diag(lam2)) < 60*Meps,
abs(sm - V2n %*% diag(lam2) %*% t(V2n)) < 60*Meps)
## Re-ordered as with symmetric:
sV <- V2n[,i]
slam <- lam2[i]
all(abs(sm %*% sV - sV %*% diag(slam)) < 60*Meps)
all(abs(sm - sV %*% diag(slam) %*% t(sV)) < 60*Meps)
## sV *is* now equal to V -- up to sign (+-) and rounding errors
all(abs(c(1 - abs(sV / V))) < 1000*Meps) # TRUE (P ~ 0.95)