expm

0th

Percentile

Matrix Exponential

This function computes the exponential of a square matrix $A$, defined as the sum from $r=0$ to infinity of $A^r/r!$. Several methods are provided. The Taylor series and PadÃ© approximation are very importantly combined with scaling and squaring.

Keywords
math, algebra
Usage
expm(x, method = c("Higham08.b", "Higham08",
order = 8, trySym = TRUE, tol = .Machine\$double.eps,
preconditioning = c("2bal", "1bal", "buggy"))
Arguments
x
a square matrix.
method
"Higham08.b", "Ward77", "Pade" or "Taylor", etc; The default is now "Higham08.b" which uses Higham's 2008 algorithm with additional balancing preconditioning, see
order
an integer, the order of approximation to be used, for the "Pade" and "Taylor" methods. The best value for this depends on machine precision (and slightly on x) but for the current double precision arithmetic, one recommendation
trySym
logical indicating if method = "R_Eigen" should use isSymmetric(x) and take advantage for (almost) symmetric matrices.
tol
a given tolerance used to check if x is computationally singular when method = "hybrid_Eigen_Ward".
preconditioning
a string specifying which implementation of Ward(1977) should be used when method = "Ward77".
Details

The exponential of a matrix is defined as the infinite Taylor series $$e^M = \sum_{k = 1}^\infty \frac{M^k}{k!}.$$

For the "Pade" and "Taylor" methods, there is an "accuracy" attribute of the result. It is an upper bound for the L2 norm of the Cauchy error expm(x, *, order + 10) - expm(x, *, order).

Value

• The matrix exponential of x.

Note

For a good general discussion of the matrix exponential problem, see Moler and van Loan (2003).

UTF-8

References

Ward, R. C. (1977). Numerical computation of the matrix exponential with accuracy estimate. SIAM J. Num. Anal. 14, 600--610.

Moler, C and van Loan, C (2003). Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later. SIAM Review 45, 3--49. At http://epubs.siam.org/sam-bin/dbq/article/41801

The package vignette for details on the algorithms and calling the function from external packages.

• expm
• mexp
Examples
x <- matrix(c(-49, -64, 24, 31), 2, 2)
expm(x)

## ----------------------------
## Test case 1 from Ward (1977)
## ----------------------------
test1 <- t(matrix(c(
4, 2, 0,
1, 4, 1,
1, 1, 4), 3, 3))
## Results on Power Mac G3 under Mac OS 10.2.8
##                    [,1]               [,2]               [,3]
## [1,] 147.86662244637000 183.76513864636857  71.79703239999643
## [2,] 127.78108552318250 183.76513864636877  91.88256932318409
## [3,] 127.78108552318204 163.67960172318047 111.96810624637124
## -- these agree with ward (1977, p608)

## Compare with the naive "R_Eigen" method:
try(
expm(test1, method="R_Eigen")
) ## now gives an error from solve !
##
## older result was
##                   [,1]                [,2]               [,3]
##[1,] 147.86662244637003  88.500223574029647 103.39983337000028
##[2,] 127.78108552318220 117.345806155250600  90.70416537273444
##[3,] 127.78108552318226  90.384173332156763 117.66579819582827
## -- hopelessly inaccurate in all but the first column.
##
##
## ----------------------------
## Test case 2 from Ward (1977)
## ----------------------------
test2 <- t(matrix(c(
29.87942128909879, .7815750847907159, -2.289519314033932,
.7815750847907159, 25.72656945571064,  8.680737820540137,
-2.289519314033932, 8.680737820540137,  34.39400925519054),
3, 3))
##                   [,1]               [,2]               [,3]
##[1,]   5496313853692357 -18231880972009844 -30475770808580828
##[2,] -18231880972009852  60605228702227024 101291842930256144
##[3,] -30475770808580840 101291842930256144 169294411240859072
## -- which agrees with Ward (1977) to 13 significant figures
expm(test2, method="R_Eigen")
##                   [,1]               [,2]               [,3]
##[1,]   5496313853692405 -18231880972009100 -30475770808580196
##[2,] -18231880972009160  60605228702221760 101291842930249376
##[3,] -30475770808580244 101291842930249200 169294411240850880
## -- in this case a very similar degree of accuracy.
##
## ----------------------------
## Test case 3 from Ward (1977)
## ----------------------------
test3 <- t(matrix(c(
-131, 19, 18,
-390, 56, 54,
-387, 57, 52), 3, 3))
##                    [,1]                [,2]                [,3]
##[1,] -1.5096441587713636 0.36787943910439874 0.13533528117301735
##[2,] -5.6325707997970271 1.47151775847745725 0.40600584351567010
##[3,] -4.9349383260294299 1.10363831731417195 0.54134112675653534
## -- agrees to 10dp with Ward (1977), p608.
expm(test3, method="R_Eigen")
##                   [,1]               [,2]                [,3]
##[1,] -1.509644158796182 0.3678794391103086 0.13533528117547022
##[2,] -5.632570799902948 1.4715177585023838 0.40600584352641989
##[3,] -4.934938326098410 1.1036383173309319 0.54134112676302582
## -- in this case, a similar level of agreement with Ward (1977).
##
## ----------------------------
## Test case 4 from Ward (1977)
## ----------------------------
test4 <-
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-10,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0),
.Dim = c(10, 10))
##[1] 8.746826694186494e-08
## -- here mexp2 is accurate only to 7 d.p., whereas mexp
##    is correct to at least 14 d.p.
##
## Note that these results are achieved with the default
## settings order=8, method="Pade" -- accuracy could
## presumably be improved still further by some tuning
## of these settings.

##
## example of computationally singular matrix
##
m <- matrix(c(0,1,0,0), 2,2)
try(
expm(m, m="R_Eigen")
)
## error since m is computationally singular
expm(m, m="hybrid")
## hybrid use the Ward77 method
Documentation reproduced from package expm, version 0.98-1, License: GPL (>= 2)

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