Convex Optimization in R by convexjlr

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convexjlr is an R package for Disciplined Convex Programming (DCP) by providing a high level wrapper for Julia package Convex.jl. The aim is to provide optimization results rapidly and reliably in R once you formulate your problem as a convex problem. convexjlr can solve linear programs, second order cone programs, semidefinite programs, exponential cone programs, mixed-integer linear programs, and some other DCP-compliant convex programs through Convex.jl.


convexjlr is on CRAN now! To use package convexjlr, you first have to install Julia on your computer, and then you can install convexjlr just like any other R packages.

Note: convexjlr supports multiple ways to connect to julia, one way is through package XRJulia and the other way is to use package JuliaCall. The differences are as follows:

We hope you use convexjlr to solve your own problems. If you would like to share your experience on using convexjlr or have any questions about convexjlr, don’t hesitate to contact me:

Quick Example

We will show a short example for convexjlr in solving linear regression problem. To use package convexjlr, we first need to attach it and do the initial setup:

#> Attaching package: 'convexjlr'
#> The following object is masked from 'package:base':
#>     norm
## If you wish to use JuliaCall backend for performance
convex_setup(backend = "JuliaCall")
#> Doing initialization. It may take some time. Please wait.
#> Julia version 0.6.2 at location /Applications/ will be used.
#> Julia initiation...
#> Finish Julia initiation.
#> Loading setup script for JuliaCall...
#> Finish loading setup script for JuliaCall.
#> [1] TRUE

And this is our linear regression function using convexjlr:

linear_regression <- function(x, y){
    p <- ncol(x)
    ## n is a scalar, you don't have to use J(.) to send it to Julia.
    n <- nrow(x) ## n <- J(nrow(x))
    ## x is a matrix and y is a vector, you have to use J(.) to send them to Julia.
    x <- J(x)
    y <- J(y)
    ## coefficient vector beta and intercept b.
    beta <- Variable(p)
    b <- Variable()
    ## MSE is mean square error.
    MSE <- Expr(sumsquares(y - x %*% beta - b) / n)
    ## In linear regression, we want to minimize MSE.
    p1 <- minimize(MSE)
    list(coef = value(beta), intercept = value(b))

In the function, x is the predictor matrix, y is the response we have. And the linear_regression function will return the coefficient and intercept solved by cvx_optim.

Now we can see a little example using the linear_regression function we have just built.

n <- 1000
p <- 5
## Sigma, the covariance matrix of x, is of AR-1 strcture.
Sigma <- outer(1:p, 1:p, function(i, j) 0.5 ^ abs(i - j))
x <- matrix(rnorm(n * p), n, p) %*% chol(Sigma)
## The real coefficient is all zero except the first, second and fourth elements.
beta0 <- c(5, 1, 0, 2, 0)
y <- x %*% beta0 + 0.2 * rnorm(n)

linear_regression(x, y)$coef
#>              [,1]
#> [1,]  5.003240727
#> [2,]  0.991592939
#> [3,] -0.013119040
#> [4,]  2.008251896
#> [5,]  0.004306522

More Examples

More examples (including using convexjlr for Lasso, logistic regression and Support Vector Machine) can be found in the pakage vignette or on the github page: