Disciplined Convex Optimization in R

What is CVXR?

CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the restrictive standard form required by most solvers. The user specifies an objective and set of constraints by combining constants, variables, and parameters using a library of functions with known mathematical properties. CVXR then applies signed disciplined convex programming (DCP) to verify the problem’s convexity. Once verified, the problem is converted into standard form using graph implementations and passed to a convex solver such as OSQP or ECOS or SCS.

Where can I learn more?

The paper by Fu, Narasimhan, and Boyd (2020) is the main reference. Further documentation, along with a number of tutorial examples, is also available on the CVXR website.

Below we provide a simple example to get you started.

A Simple Example

Consider a simple linear regression problem where it is desired to estimate a set of parameters using a least squares criterion.

We generate some synthetic data where we know the model completely, that is

Y = Xβ + ϵ

where Y is a 100 × 1 vector, X is a 100 × 10 matrix, β = [−4, …, −1, 0, 1, …, 5] is a 10 × 1 vector, and ϵ ∼ N(0, 1).

set.seed(123)

n <- 100
p <- 10
beta <- -4:5   # beta is just -4 through 5.

X <- matrix(rnorm(n * p), nrow=n)
colnames(X) <- paste0("beta_", beta)
Y <- X %*% beta + rnorm(n)

Given the data X and Y, we can estimate the β vector using lm function in R that fits a standard regression model.

ls.model <- lm(Y ~ 0 + X)   # There is no intercept in our model above
m <- data.frame(ls.est = coef(ls.model))
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m)

These are the least-squares estimates and can be seen to be reasonably close to the original β values -4 through 5.

The CVXR formulation

The CVXR formulation states the above as an optimization problem:

$$ \begin{array}{ll} \underset{\beta}{\mbox{minimize}} & \|y - X\beta\|_2^2, \end{array} $$ which directly translates into a problem that CVXR can solve as shown in the steps below.

• Step 0. Load the CVXR library

suppressWarnings(library(CVXR, warn.conflicts=FALSE))

• Step 1. Define the variable to be estimated

betaHat <- Variable(p)

• Step 2. Define the objective to be optimized

objective <- Minimize(sum((Y - X %*% betaHat)^2))

Notice how the objective is specified using functions such as sum, *%* and ^, that are familiar to R users despite that fact that betaHat is no ordinary R expression but a CVXR expression.

• Step 3. Create a problem to solve

problem <- Problem(objective)

• Step 4. Solve it!

result <- solve(problem)

• Step 5. Extract solution and objective value

## Objective value: 97.847586

We can indeed satisfy ourselves that the results we get matches that from lm.

m <- cbind(coef(ls.model), result$getValue(betaHat))
colnames(m) <- c("lm est.", "CVXR est.")
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m)

Wait a minute! What have we gained?

On the surface, it appears that we have replaced one call to lm with at least five or six lines of new R code. On top of that, the code actually runs slower, and so it is not clear what was really achieved.

So suppose we knew for a fact that the βs were nonnegative and we wish to take this fact into account. This is nonnegative least squares regression and lm would no longer do the job.

In CVXR, the modified problem merely requires the addition of a constraint to the problem definition.

problem <- Problem(objective, constraints = list(betaHat >= 0))
result <- solve(problem)
m <- data.frame(CVXR.est = result$getValue(betaHat))
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m)

We can verify once again that these values are comparable to those obtained from another R package, say nnls.

if (requireNamespace("nnls", quietly = TRUE)) {
    nnls.fit <- nnls::nnls(X, Y)$x
} else {
    nnls.fit <- rep(NA, p)
}

m <- cbind(result$getValue(betaHat), nnls.fit)
colnames(m) <- c("CVXR est.", "nnls est.")
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m)

Okay that was cool, but…

As you no doubt noticed, we have done nothing that other R packages could not do.

So now suppose that we know, for some extraneous reason, that the sum of β₂ and β₃ is nonpositive and but all other βs are nonnegative.

It is clear that this problem would not fit into any standard package. But in CVXR, this is easily done by adding a few constraints.

To express the fact that β₂ + β₃ is nonpositive, we construct a row matrix with zeros everywhere, except in positions 2 and 3 (for β₂ and β₃ respectively).

A <- matrix(c(0, 1, 1, rep(0, 7)), nrow = 1)
colnames(A) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(A)

The sum constraint is nothing but Aβ <  = 0

which we express in R as

constraint1 <- A %*% betaHat <= 0

NOTE: The above constraint can also be expressed simply as

constraint1 <- betaHat[2] + betaHat[3] <= 0

but it is easier working with matrices in general with CVXR.

For the nonnegativity for rest of the variables, we construct a 10 × 10 matrix A to have 1’s along the diagonal everywhere except rows 2 and 3 and zeros everywhere.

B <- diag(c(1, 0, 0, rep(1, 7)))
colnames(B) <- rownames(B) <- paste0("$\\beta_{", 1:p, "}$")
    knitr::kable(B)

The constraint for positivity is Bβ > 0

which we express in R as

constraint2 <- B %*% betaHat >= 0

Now we are ready to solve the problem just as before.

problem <- Problem(objective, constraints = list(constraint1, constraint2))
result <- solve(problem)

And we can get the estimates of β.

m <- data.frame(CVXR.soln = result$getValue(betaHat))
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m)

This demonstrates the chief advantage of CVXR: flexibility. Users can quickly modify and re-solve a problem, making our package ideal for prototyping new statistical methods. Its syntax is simple and mathematically intuitive. Furthermore, CVXR combines seamlessly with native R code as well as several popular packages, allowing it to be incorporated easily into a larger analytical framework. The user is free to construct statistical estimators that are solutions to a convex optimization problem where there may not be a closed form solution or even an implementation. Such solutions can then be combined with resampling techniques like the bootstrap to estimate variability.