README.md
In yixuan/ADMM: Solving Statistical Optimization Problems Using the ADMM Algorithm
Introduction
ADMM is an R package that utilizes the Alternating Direction Method of Multipliers
(ADMM) algorithm to solve a broad range of statistical optimization problems.
Presently the models that ADMM has implemented include Lasso, Elastic Net,
Least Absolute Deviation and Basis Pursuit.
Installation
ADMM package is still experimental, so it has not been submitted to CRAN yet.
To install ADMM, you need a C++ compiler such as g++ or clang++, and
for Windows users, the
Rtools
software is needed (unless you can configure the toolchain by yourself).
The installation follows the typical way of R packages on Github:
library(devtools)
install_github("yixuan/ADMM")
In order to achieve high performance computation, the package uses
single precision floating point type (aka float) in some part of the code,
which uses single precision BLAS functions such as ssyrk(). However,
the BLAS library shipped with R does not contain such functions, so for
code portability, a macro called NO_FLOAT_BLAS is defined in
src/Makevars to use fallback Eigen implementation.
If your R is linking to a high performance BLAS such as
OpenBLAS, it is suggested to remove that macro in
order to use the BLAS implementation. Moreover, the Eigen library that ADMM
pakcage depends on benefits from modern CPU's vectorization support, so it is
also recommended to define -msse4 or -mavx in the PKG_CXXFLAGS variable
(in file src/Makevars) whenever applicable.
Models
Lasso
library(glmnet)
library(ADMM)
set.seed(123)
n <- 100
p <- 20
m <- 5
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, mean = 1.2, sd = 2), n, p)
y <- 5 + x %*% b + rnorm(n)
fit <- glmnet(x, y)
out_glmnet <- coef(fit, s = exp(-2), exact = TRUE)
out_admm <- admm_lasso(x, y)$penalty(exp(-2))$fit()
out_paradmm <- admm_lasso(x, y)$penalty(exp(-2))$parallel()$fit()
data.frame(glmnet = as.numeric(out_glmnet),
admm = as.numeric(out_admm$beta),
paradmm = as.numeric(out_paradmm$beta))
## glmnet admm paradmm
## 1 5.357410774 5.357455254 5.357429504
## 2 0.178916019 0.178915471 0.178917870
## 3 0.683606818 0.683609307 0.683610320
## 4 0.310518550 0.310507625 0.310525119
## 5 0.861034415 0.861029863 0.861012816
## 6 0.879797912 0.879794598 0.879801810
## 7 0.007854581 0.007850002 0.007853498
## 8 0.000000000 0.000000000 0.000000000
## 9 0.000000000 0.000000000 0.000000000
## 10 0.023462980 0.023467677 0.023452930
## 11 0.010952896 0.010957017 0.010950469
## 12 0.000000000 0.000000000 0.000000000
## 13 -0.003800159 -0.003811116 -0.003801103
## 14 0.000000000 0.000000000 0.000000000
## 15 0.094591923 0.094586611 0.094600648
## 16 0.000000000 0.000000000 0.000000000
## 17 0.000000000 0.000000000 0.000000000
## 18 0.000000000 0.000000000 0.000000000
## 19 0.000000000 0.000000000 0.000000000
## 20 -0.002916255 -0.002929136 -0.002919935
## 21 0.000000000 0.000000000 0.000000000
Elastic Net
fit <- glmnet(x, y, alpha = 0.5)
out_glmnet <- coef(fit, s = exp(-2), exact = TRUE)
out_admm <- admm_enet(x, y)$penalty(exp(-2), alpha = 0.5)$fit()
data.frame(glmnet = as.numeric(out_glmnet),
admm = as.numeric(out_admm$beta))
## glmnet admm
## 1 5.150556538 5.150497437
## 2 0.204543779 0.204526767
## 3 0.705652674 0.705665767
## 4 0.330650192 0.330640256
## 5 0.872594728 0.872611761
## 6 0.884433876 0.884422064
## 7 0.048044107 0.048055928
## 8 0.025072878 0.025097074
## 9 0.000000000 0.000000000
## 10 0.057804317 0.057830613
## 11 0.041853068 0.041876025
## 12 -0.004476248 -0.004499977
## 13 -0.035255637 -0.035279647
## 14 0.000000000 0.000000000
## 15 0.110919341 0.110915266
## 16 0.000000000 0.000000000
## 17 0.000000000 0.000000000
## 18 0.000000000 0.000000000
## 19 0.000000000 0.000000000
## 20 -0.021003756 -0.020984368
## 21 0.000000000 0.000000000
Least Absolute Deviation
Least Absolute Deviation (LAD) minimizes ||y - Xb||_1 instead of
||y - Xb||_2^2 (OLS), and is equivalent to median regression.
library(quantreg)
out_rq <- rq.fit(x, y)
out_admm <- admm_lad(x, y, intercept = FALSE)$fit()
data.frame(rq_br = out_rq$coefficients,
admm = out_admm$beta[-1])
## rq_br admm
## 1 0.463871497 0.4630289961
## 2 0.829243353 0.8324149339
## 3 0.151432833 0.1493799430
## 4 1.074107564 1.0707590072
## 5 0.958979798 0.9569585188
## 6 0.502539859 0.5028832829
## 7 0.337640338 0.3360263689
## 8 0.209127703 0.2120946512
## 9 0.361765382 0.3630356485
## 10 0.323168985 0.3217875563
## 11 -0.002009264 0.0007319653
## 12 -0.036099511 -0.0370447075
## 13 0.328007777 0.3290499302
## 14 0.296038071 0.2992857234
## 15 0.310187867 0.3117528782
## 16 0.071713681 0.0711670377
## 17 0.166827429 0.1622600454
## 18 0.260366502 0.2580854533
## 19 0.324487629 0.3251952295
## 20 0.209758565 0.2131039214
Basis Pursuit
set.seed(123)
n <- 50
p <- 100
nsig <- 15
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
out_admm <- admm_bp(x, y)$fit()
range(beta_true - out_admm$beta)
## [1] -0.0006052779 0.0004780069
Performance
Lasso and Elastic Net
library(microbenchmark)
library(ADMM)
library(glmnet)
# compute the full solution path, n > p
set.seed(123)
n <- 10000
p <- 1000
m <- 100
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
lambdas1 = glmnet(x, y)$lambda
lambdas2 = glmnet(x, y, alpha = 0.6)$lambda
microbenchmark(
"glmnet[lasso]" = {res1 <- glmnet(x, y)},
"admm[lasso]" = {res2 <- admm_lasso(x, y)$penalty(lambdas1)$fit()},
"padmm[lasso]" = {res3 <- admm_lasso(x, y)$penalty(lambdas1)$parallel()$fit()},
"glmnet[enet]" = {res4 <- glmnet(x, y, alpha = 0.6)},
"admm[enet]" = {res5 <- admm_enet(x, y)$penalty(lambdas2, alpha = 0.6)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq max neval
## glmnet[lasso] 939.0194 943.7227 1005.1232 1043.2826 1048.1939 1051.3973 5
## admm[lasso] 320.1375 320.7603 321.6413 321.0283 321.0635 325.2172 5
## padmm[lasso] 486.7478 510.7104 529.9721 512.5421 567.0847 572.7757 5
## glmnet[enet] 950.9513 1048.1872 1031.4437 1049.9402 1051.2483 1056.8917 5
## admm[enet] 286.9177 288.8735 289.0231 288.9758 289.1651 291.1834 5
# difference of results
diffs = matrix(0, 3, 2)
rownames(diffs) = c("glmnet-admm [lasso]", "glmnet-padmm[lasso]", "glmnet-admm [enet]")
colnames(diffs) = c("min", "max")
diffs[1, ] = range(coef(res1) - res2$beta)
diffs[2, ] = range(coef(res1) - res3$beta)
diffs[3, ] = range(coef(res4) - res5$beta)
diffs
## min max
## glmnet-admm [lasso] -0.0002873333 7.259293e-05
## glmnet-padmm[lasso] -0.0005554722 7.382258e-05
## glmnet-admm [enet] -0.0002195360 8.176991e-05
# p > n
set.seed(123)
n <- 1000
p <- 2000
m <- 100
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
lambdas1 = glmnet(x, y)$lambda
lambdas2 = glmnet(x, y, alpha = 0.6)$lambda
microbenchmark(
"glmnet[lasso]" = {res1 <- glmnet(x, y)},
"admm[lasso]" = {res2 <- admm_lasso(x, y)$penalty(lambdas1)$fit()},
"padmm[lasso]" = {res3 <- admm_lasso(x, y)$penalty(lambdas1)$parallel()$fit()},
"glmnet[enet]" = {res4 <- glmnet(x, y, alpha = 0.6)},
"admm[enet]" = {res5 <- admm_enet(x, y)$penalty(lambdas2, alpha = 0.6)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq max neval
## glmnet[lasso] 197.9279 198.2767 199.1576 199.3774 200.0559 200.1499 5
## admm[lasso] 230.6332 237.1298 245.8944 247.4240 250.6257 263.6596 5
## padmm[lasso] 5159.2198 5170.7308 5513.3797 5345.6322 5426.8846 6464.4313 5
## glmnet[enet] 195.7102 196.7432 197.3977 197.7577 198.3421 198.4355 5
## admm[enet] 225.8397 239.6536 247.2756 249.9269 252.2080 268.7499 5
# difference of results
diffs[1, ] = range(coef(res1) - res2$beta)
diffs[2, ] = range(coef(res1) - res3$beta)
diffs[3, ] = range(coef(res4) - res5$beta)
diffs
## min max
## glmnet-admm [lasso] -0.001518947 0.002055109
## glmnet-padmm[lasso] -0.001898237 0.002052009
## glmnet-admm [enet] -0.001615556 0.001948477
LAD
library(ADMM)
library(quantreg)
set.seed(123)
n <- 1000
p <- 500
b <- runif(p)
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
microbenchmark(
"quantreg[br]" = {res1 <- rq.fit(x, y)},
"quantreg[fn]" = {res2 <- rq.fit(x, y, method = "fn")},
"admm" = {res3 <- admm_lad(x, y, intercept = FALSE)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq
## quantreg[br] 2420.34862 2422.79215 2493.76695 2426.54150 2429.49592
## quantreg[fn] 451.87866 452.94572 454.71406 453.56378 455.81717
## admm 50.55354 51.17922 51.69386 51.59099 52.32357
## max neval
## 2769.65653 5
## 459.36498 5
## 52.82196 5
# difference of results
range(res1$coefficients - res3$beta[-1])
## [1] -0.006989109 0.006061505
set.seed(123)
n <- 5000
p <- 1000
b <- runif(p)
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
microbenchmark(
"quantreg[fn]" = {res1 <- rq.fit(x, y, method = "fn")},
"admm" = {res2 <- admm_lad(x, y, intercept = FALSE)$fit()},
times = 5
)
## Unit: seconds
## expr min lq mean median uq max neval
## quantreg[fn] 6.156911 6.231430 7.686811 6.280464 9.861437 9.903813 5
## admm 2.184311 2.187431 2.193200 2.189173 2.202042 2.203044 5
# difference of results
range(res1$coefficients - res2$beta[-1])
## [1] -0.003577610 0.004135838
Basis Pursuit
set.seed(123)
n <- 1000
p <- 2000
nsig <- 100
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
system.time(out_admm <- admm_bp(x, y)$fit())
## user system elapsed
## 0.996 0.169 0.292
range(beta_true - out_admm$beta)
## [1] -0.001267782 0.002108828
set.seed(123)
n <- 1000
p <- 10000
nsig <- 200
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
system.time(out_admm <- admm_bp(x, y)$fit())
## user system elapsed
## 19.315 0.573 4.969
range(beta_true - out_admm$beta)
## [1] -0.1575968 0.3361001
yixuan/ADMM documentation built on May 4, 2019, 5:28 p.m.
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Introduction
ADMM is an R package that utilizes the Alternating Direction Method of Multipliers
(ADMM) algorithm to solve a broad range of statistical optimization problems.
Presently the models that ADMM has implemented include Lasso, Elastic Net,
Least Absolute Deviation and Basis Pursuit.
Installation
ADMM package is still experimental, so it has not been submitted to CRAN yet.
To install ADMM, you need a C++ compiler such as g++ or clang++, and
for Windows users, the
Rtools
software is needed (unless you can configure the toolchain by yourself).
The installation follows the typical way of R packages on Github:
library(devtools)
install_github("yixuan/ADMM")
In order to achieve high performance computation, the package uses
single precision floating point type (aka float) in some part of the code,
which uses single precision BLAS functions such as ssyrk(). However,
the BLAS library shipped with R does not contain such functions, so for
code portability, a macro called NO_FLOAT_BLAS is defined in
src/Makevars to use fallback Eigen implementation.
If your R is linking to a high performance BLAS such as
OpenBLAS, it is suggested to remove that macro in
order to use the BLAS implementation. Moreover, the Eigen library that ADMM
pakcage depends on benefits from modern CPU's vectorization support, so it is
also recommended to define -msse4 or -mavx in the PKG_CXXFLAGS variable
(in file src/Makevars) whenever applicable.
Models
Lasso
library(glmnet)
library(ADMM)
set.seed(123)
n <- 100
p <- 20
m <- 5
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, mean = 1.2, sd = 2), n, p)
y <- 5 + x %*% b + rnorm(n)
fit <- glmnet(x, y)
out_glmnet <- coef(fit, s = exp(-2), exact = TRUE)
out_admm <- admm_lasso(x, y)$penalty(exp(-2))$fit()
out_paradmm <- admm_lasso(x, y)$penalty(exp(-2))$parallel()$fit()
data.frame(glmnet = as.numeric(out_glmnet),
admm = as.numeric(out_admm$beta),
paradmm = as.numeric(out_paradmm$beta))
## glmnet admm paradmm
## 1 5.357410774 5.357455254 5.357429504
## 2 0.178916019 0.178915471 0.178917870
## 3 0.683606818 0.683609307 0.683610320
## 4 0.310518550 0.310507625 0.310525119
## 5 0.861034415 0.861029863 0.861012816
## 6 0.879797912 0.879794598 0.879801810
## 7 0.007854581 0.007850002 0.007853498
## 8 0.000000000 0.000000000 0.000000000
## 9 0.000000000 0.000000000 0.000000000
## 10 0.023462980 0.023467677 0.023452930
## 11 0.010952896 0.010957017 0.010950469
## 12 0.000000000 0.000000000 0.000000000
## 13 -0.003800159 -0.003811116 -0.003801103
## 14 0.000000000 0.000000000 0.000000000
## 15 0.094591923 0.094586611 0.094600648
## 16 0.000000000 0.000000000 0.000000000
## 17 0.000000000 0.000000000 0.000000000
## 18 0.000000000 0.000000000 0.000000000
## 19 0.000000000 0.000000000 0.000000000
## 20 -0.002916255 -0.002929136 -0.002919935
## 21 0.000000000 0.000000000 0.000000000
Elastic Net
fit <- glmnet(x, y, alpha = 0.5)
out_glmnet <- coef(fit, s = exp(-2), exact = TRUE)
out_admm <- admm_enet(x, y)$penalty(exp(-2), alpha = 0.5)$fit()
data.frame(glmnet = as.numeric(out_glmnet),
admm = as.numeric(out_admm$beta))
## glmnet admm
## 1 5.150556538 5.150497437
## 2 0.204543779 0.204526767
## 3 0.705652674 0.705665767
## 4 0.330650192 0.330640256
## 5 0.872594728 0.872611761
## 6 0.884433876 0.884422064
## 7 0.048044107 0.048055928
## 8 0.025072878 0.025097074
## 9 0.000000000 0.000000000
## 10 0.057804317 0.057830613
## 11 0.041853068 0.041876025
## 12 -0.004476248 -0.004499977
## 13 -0.035255637 -0.035279647
## 14 0.000000000 0.000000000
## 15 0.110919341 0.110915266
## 16 0.000000000 0.000000000
## 17 0.000000000 0.000000000
## 18 0.000000000 0.000000000
## 19 0.000000000 0.000000000
## 20 -0.021003756 -0.020984368
## 21 0.000000000 0.000000000
Least Absolute Deviation
Least Absolute Deviation (LAD) minimizes ||y - Xb||_1 instead of
||y - Xb||_2^2 (OLS), and is equivalent to median regression.
library(quantreg)
out_rq <- rq.fit(x, y)
out_admm <- admm_lad(x, y, intercept = FALSE)$fit()
data.frame(rq_br = out_rq$coefficients,
admm = out_admm$beta[-1])
## rq_br admm
## 1 0.463871497 0.4630289961
## 2 0.829243353 0.8324149339
## 3 0.151432833 0.1493799430
## 4 1.074107564 1.0707590072
## 5 0.958979798 0.9569585188
## 6 0.502539859 0.5028832829
## 7 0.337640338 0.3360263689
## 8 0.209127703 0.2120946512
## 9 0.361765382 0.3630356485
## 10 0.323168985 0.3217875563
## 11 -0.002009264 0.0007319653
## 12 -0.036099511 -0.0370447075
## 13 0.328007777 0.3290499302
## 14 0.296038071 0.2992857234
## 15 0.310187867 0.3117528782
## 16 0.071713681 0.0711670377
## 17 0.166827429 0.1622600454
## 18 0.260366502 0.2580854533
## 19 0.324487629 0.3251952295
## 20 0.209758565 0.2131039214
Basis Pursuit
set.seed(123)
n <- 50
p <- 100
nsig <- 15
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
out_admm <- admm_bp(x, y)$fit()
range(beta_true - out_admm$beta)
## [1] -0.0006052779 0.0004780069
Performance
Lasso and Elastic Net
library(microbenchmark)
library(ADMM)
library(glmnet)
# compute the full solution path, n > p
set.seed(123)
n <- 10000
p <- 1000
m <- 100
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
lambdas1 = glmnet(x, y)$lambda
lambdas2 = glmnet(x, y, alpha = 0.6)$lambda
microbenchmark(
"glmnet[lasso]" = {res1 <- glmnet(x, y)},
"admm[lasso]" = {res2 <- admm_lasso(x, y)$penalty(lambdas1)$fit()},
"padmm[lasso]" = {res3 <- admm_lasso(x, y)$penalty(lambdas1)$parallel()$fit()},
"glmnet[enet]" = {res4 <- glmnet(x, y, alpha = 0.6)},
"admm[enet]" = {res5 <- admm_enet(x, y)$penalty(lambdas2, alpha = 0.6)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq max neval
## glmnet[lasso] 939.0194 943.7227 1005.1232 1043.2826 1048.1939 1051.3973 5
## admm[lasso] 320.1375 320.7603 321.6413 321.0283 321.0635 325.2172 5
## padmm[lasso] 486.7478 510.7104 529.9721 512.5421 567.0847 572.7757 5
## glmnet[enet] 950.9513 1048.1872 1031.4437 1049.9402 1051.2483 1056.8917 5
## admm[enet] 286.9177 288.8735 289.0231 288.9758 289.1651 291.1834 5
# difference of results
diffs = matrix(0, 3, 2)
rownames(diffs) = c("glmnet-admm [lasso]", "glmnet-padmm[lasso]", "glmnet-admm [enet]")
colnames(diffs) = c("min", "max")
diffs[1, ] = range(coef(res1) - res2$beta)
diffs[2, ] = range(coef(res1) - res3$beta)
diffs[3, ] = range(coef(res4) - res5$beta)
diffs
## min max
## glmnet-admm [lasso] -0.0002873333 7.259293e-05
## glmnet-padmm[lasso] -0.0005554722 7.382258e-05
## glmnet-admm [enet] -0.0002195360 8.176991e-05
# p > n
set.seed(123)
n <- 1000
p <- 2000
m <- 100
b <- matrix(c(runif(m), rep(0, p - m)))
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
lambdas1 = glmnet(x, y)$lambda
lambdas2 = glmnet(x, y, alpha = 0.6)$lambda
microbenchmark(
"glmnet[lasso]" = {res1 <- glmnet(x, y)},
"admm[lasso]" = {res2 <- admm_lasso(x, y)$penalty(lambdas1)$fit()},
"padmm[lasso]" = {res3 <- admm_lasso(x, y)$penalty(lambdas1)$parallel()$fit()},
"glmnet[enet]" = {res4 <- glmnet(x, y, alpha = 0.6)},
"admm[enet]" = {res5 <- admm_enet(x, y)$penalty(lambdas2, alpha = 0.6)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq max neval
## glmnet[lasso] 197.9279 198.2767 199.1576 199.3774 200.0559 200.1499 5
## admm[lasso] 230.6332 237.1298 245.8944 247.4240 250.6257 263.6596 5
## padmm[lasso] 5159.2198 5170.7308 5513.3797 5345.6322 5426.8846 6464.4313 5
## glmnet[enet] 195.7102 196.7432 197.3977 197.7577 198.3421 198.4355 5
## admm[enet] 225.8397 239.6536 247.2756 249.9269 252.2080 268.7499 5
# difference of results
diffs[1, ] = range(coef(res1) - res2$beta)
diffs[2, ] = range(coef(res1) - res3$beta)
diffs[3, ] = range(coef(res4) - res5$beta)
diffs
## min max
## glmnet-admm [lasso] -0.001518947 0.002055109
## glmnet-padmm[lasso] -0.001898237 0.002052009
## glmnet-admm [enet] -0.001615556 0.001948477
LAD
library(ADMM)
library(quantreg)
set.seed(123)
n <- 1000
p <- 500
b <- runif(p)
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
microbenchmark(
"quantreg[br]" = {res1 <- rq.fit(x, y)},
"quantreg[fn]" = {res2 <- rq.fit(x, y, method = "fn")},
"admm" = {res3 <- admm_lad(x, y, intercept = FALSE)$fit()},
times = 5
)
## Unit: milliseconds
## expr min lq mean median uq
## quantreg[br] 2420.34862 2422.79215 2493.76695 2426.54150 2429.49592
## quantreg[fn] 451.87866 452.94572 454.71406 453.56378 455.81717
## admm 50.55354 51.17922 51.69386 51.59099 52.32357
## max neval
## 2769.65653 5
## 459.36498 5
## 52.82196 5
# difference of results
range(res1$coefficients - res3$beta[-1])
## [1] -0.006989109 0.006061505
set.seed(123)
n <- 5000
p <- 1000
b <- runif(p)
x <- matrix(rnorm(n * p, sd = 2), n, p)
y <- x %*% b + rnorm(n)
microbenchmark(
"quantreg[fn]" = {res1 <- rq.fit(x, y, method = "fn")},
"admm" = {res2 <- admm_lad(x, y, intercept = FALSE)$fit()},
times = 5
)
## Unit: seconds
## expr min lq mean median uq max neval
## quantreg[fn] 6.156911 6.231430 7.686811 6.280464 9.861437 9.903813 5
## admm 2.184311 2.187431 2.193200 2.189173 2.202042 2.203044 5
# difference of results
range(res1$coefficients - res2$beta[-1])
## [1] -0.003577610 0.004135838
Basis Pursuit
set.seed(123)
n <- 1000
p <- 2000
nsig <- 100
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
system.time(out_admm <- admm_bp(x, y)$fit())
## user system elapsed
## 0.996 0.169 0.292
range(beta_true - out_admm$beta)
## [1] -0.001267782 0.002108828
set.seed(123)
n <- 1000
p <- 10000
nsig <- 200
beta_true <- c(runif(nsig), rep(0, p - nsig))
beta_true <- sample(beta_true)
x <- matrix(rnorm(n * p), n, p)
y <- drop(x %*% beta_true)
system.time(out_admm <- admm_bp(x, y)$fit())
## user system elapsed
## 19.315 0.573 4.969
range(beta_true - out_admm$beta)
## [1] -0.1575968 0.3361001
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