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Originally, I entitled this package Fast Matrix Algebra with ‘Eigen’, because I expected it to be faster than R base. But this is not the case. So I entitled it Complex Matrix Algebra with ‘Eigen’, because it supports some operations on complex matrices which are not supported by R base: determinant, Cholesky decomposition, and linear least-squares problems.
set.seed(666L)
M <- matrix(rnorm(300L*300L, mean = 1), 300L, 300L)
M[sample.int(300L*300L, 300L*270L)] <- 0 # 90% of zeros
Ms <- asSparseMatrix(M)
microbenchmark(
base = det(M),
EigenR = Eigen_det(M),
EigenR_sparse = Eigen_det(Ms), # :-(
times = 200L
)
## Unit: milliseconds
## expr min lq mean median uq max neval cld
## base 6.9588 7.90125 9.475266 8.90305 10.01820 24.8761 200 a
## EigenR 2.4274 3.20710 3.913163 3.68080 4.44480 7.0813 200 b
## EigenR_sparse 4.9384 5.73910 6.760806 6.36535 7.52195 12.4549 200 cDeterminants of complex matrices are supported:
set.seed(666L)
Mr <- matrix(rnorm(100L*100L, mean = 1), 100L, 100L)
Mi <- matrix(rnorm(100L*100L, mean = 1), 100L, 100L)
M <- Mr + 1i * Mi
library(complexplus)
microbenchmark(
EigenR = Eigen_det(M), # :-)
complexplus = Det(M),
times = 30L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## EigenR 744.8 879.6 1152.00 1017.95 1281.3 2035.1 30 a
## complexplus 16028.3 17791.2 19376.53 19287.40 20139.6 27735.5 30 bset.seed(666L)
M <- matrix(rnorm(100L*100L), 100L, 100L)
microbenchmark(
base = solve(M),
EigenR = Eigen_inverse(M), # :-)
times = 500L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## base 1470.2 1699.55 2779.189 2191.10 2829.25 24528.5 500 a
## EigenR 952.3 1060.65 1907.809 1360.75 1884.10 34256.2 500 bset.seed(666L)
M <- matrix(rnorm(100L*70L), 100L, 70L)
library(MASS)
library(pracma)
microbenchmark(
MASS = ginv(M),
pracma = pinv(M),
EigenR = Eigen_pinverse(M), # :-)
times = 500L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## MASS 2982.4 3380.50 4535.176 3882.15 5018.55 34714.0 500 a
## pracma 2935.6 3372.45 4470.872 3875.75 4960.50 43498.4 500 a
## EigenR 663.2 928.75 1229.255 1014.30 1421.85 8672.9 500 bset.seed(666L)
M <- matrix(rpois(10000L, 25), 100L, 100L)
A <- crossprod(M)
microbenchmark(
base = chol(A),
EigenR = Eigen_chol(A),
times = 1000L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## base 179.2 232.70 285.8752 243.35 281.55 7893.7 1000 a
## EigenR 132.2 185.25 267.9837 200.15 260.15 8256.3 1000 aCholesky decomposition of complex matrices is supported.
set.seed(666L)
M <- matrix(rgamma(202L*199L, 10), 202L, 199L)
M <- cbind(M, M[, 1L] + 3*M[, 2L])
A <- crossprod(M)
microbenchmark(
base = chol(A, pivot = TRUE),
EigenR = Eigen_UtDU(A), # :-)
times = 1000L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## base 1449.0 1759.35 2258.477 1993.10 2475.95 13592.7 1000 a
## EigenR 695.9 1002.00 1344.194 1195.45 1554.65 10752.2 1000 bPivoted Cholesky decomposition of complex matrices is supported.
set.seed(666L)
M <- matrix(rpois(10000L, 25), 100L, 100L)
A <- cbind(M, M)
At <- t(A)
library(MASS)
microbenchmark(
MASS = Null(At),
EigenR_LU = Eigen_kernel(A, method = "LU"), # :-)
EigenR_COD = Eigen_kernel(A, method = "COD"),
times = 100L
)
## Unit: milliseconds
## expr min lq mean median uq max neval cld
## MASS 7.6587 9.73635 12.529576 10.4438 11.49775 154.0598 100 a
## EigenR_LU 1.6315 2.04355 2.782136 2.8114 3.25520 6.6788 100 b
## EigenR_COD 3.7744 5.05930 6.742621 6.4258 7.34365 20.8925 100 cset.seed(666L)
n <- 700L; p <- 200L
A <- matrix(rnorm(n * p), n, p)
b <- rnorm(n)
microbenchmark(
stats = lm.fit(A, b),
EigenR_svd = Eigen_lsSolve(A, b, method = "svd"), # :-(
EigenR_cod = Eigen_lsSolve(A, b, method = "cod"), # :-)
times = 100L
)
## Unit: milliseconds
## expr min lq mean median uq max neval cld
## stats 26.2811 28.80175 32.88743 32.01755 36.12695 48.3139 100 a
## EigenR_svd 46.7250 53.45475 62.50637 59.51725 71.55925 116.0388 100 b
## EigenR_cod 9.4010 10.84750 13.85632 12.80600 15.69025 45.6681 100 cComplex matrices A and b are supported.
set.seed(666L)
M <- matrix(rnorm(40L*40L, mean = 1), 40L, 40L)
microbenchmark(
expm = expm::expm(M),
EigenR = Eigen_exp(M), # :-)
times = 500L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## expm 992.7 1315.20 1654.1064 1387.95 1646.0 11770.1 500 a
## EigenR 214.2 236.85 308.5246 261.65 305.9 2188.7 500 bExponential of complex matrices is supported:
set.seed(666L)
Mr <- matrix(rnorm(40L*40L, mean = 1), 40L, 40L)
Mi <- matrix(rnorm(40L*40L, mean = 1), 40L, 40L)
M <- Mr + 1i * Mi
library(complexplus)
microbenchmark(
complexplus = matexp(M),
EigenR = Eigen_exp(M), # :-)
times = 500L
)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## complexplus 7037.9 8469.85 11674.841 10282.9 13375.75 196628.8 500 a
## EigenR 934.4 1137.00 1652.693 1630.9 1945.55 10435.8 500 bset.seed(666L)
M <- matrix(rnorm(200L*200L, mean = 1), 200L, 200L)
library(Matrix)
microbenchmark(
Matrix = Schur(M),
EigenR = Eigen_realSchur(M), # :-)
times = 50L
)
## Unit: milliseconds
## expr min lq mean median uq max neval cld
## Matrix 89.2812 91.9326 107.39999 101.5227 120.2015 153.9915 50 a
## EigenR 69.7336 74.0251 87.22316 78.9152 92.7015 139.6560 50 bUse Eigen_complexSchur for the complex Schur decomposition.
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