# Introduction

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

\[ Y = X\beta + \epsilon \]

where \(Y\) is a \(100\times 1\) vector, \(X\) is a \(100\times 10\) matrix, \(\beta = [-4,\ldots ,-1, 0, 1, \ldots, 5]\) is a \(10\times 1\) vector, and \(\epsilon \sim N(0, 1)\).

```
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 \(\beta\) 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 <- matrix(coef(ls.model), ncol = 1)
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
library(kableExtra)
knitr::kable(m, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:2, background = "#ececec")
```

\(\beta_{1}\) | -3.9196886 |

\(\beta_{2}\) | -3.0117048 |

\(\beta_{3}\) | -2.1248242 |

\(\beta_{4}\) | -0.8666048 |

\(\beta_{5}\) | 0.0914658 |

\(\beta_{6}\) | 0.9490454 |

\(\beta_{7}\) | 2.0764700 |

\(\beta_{8}\) | 3.1272275 |

\(\beta_{9}\) | 3.9609565 |

\(\beta_{10}\) | 5.1348845 |

These are the least-squares estimates and can be seen to be reasonably close to the original \(\beta\) 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(result$getValue(betaHat), coef(ls.model))
colnames(m) <- c("CVXR est.", "lm est.")
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:3, background = "#ececec")
```

CVXR est. | lm est. | |
---|---|---|

\(\beta_{1}\) | -3.9196886 | -3.9196886 |

\(\beta_{2}\) | -3.0117048 | -3.0117048 |

\(\beta_{3}\) | -2.1248242 | -2.1248242 |

\(\beta_{4}\) | -0.8666048 | -0.8666048 |

\(\beta_{5}\) | 0.0914658 | 0.0914658 |

\(\beta_{6}\) | 0.9490454 | 0.9490454 |

\(\beta_{7}\) | 2.0764700 | 2.0764700 |

\(\beta_{8}\) | 3.1272275 | 3.1272275 |

\(\beta_{9}\) | 3.9609565 | 3.9609565 |

\(\beta_{10}\) | 5.1348845 | 5.1348845 |

## 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 that the \(\beta\)s 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 <- matrix(result$getValue(betaHat), ncol = 1)
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:2, background = "#ececec")
```

\(\beta_{1}\) | 0.0000000 |

\(\beta_{2}\) | 0.0000000 |

\(\beta_{3}\) | 0.0000000 |

\(\beta_{4}\) | 0.0000000 |

\(\beta_{5}\) | 1.2374488 |

\(\beta_{6}\) | 0.6234665 |

\(\beta_{7}\) | 2.1230663 |

\(\beta_{8}\) | 2.8035640 |

\(\beta_{9}\) | 4.4448016 |

\(\beta_{10}\) | 5.2073521 |

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

```
library(nnls)
nnls.fit <- nnls(X, Y)$x
```

```
m <- cbind(result$getValue(betaHat), nnls.fit)
colnames(m) <- c("CVXR est.", "nnls est.")
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:3, background = "#ececec")
```

CVXR est. | nnls est. | |
---|---|---|

\(\beta_{1}\) | 0.0000000 | 0.0000000 |

\(\beta_{2}\) | 0.0000000 | 0.0000000 |

\(\beta_{3}\) | 0.0000000 | 0.0000000 |

\(\beta_{4}\) | 0.0000000 | 0.0000000 |

\(\beta_{5}\) | 1.2374488 | 1.2374488 |

\(\beta_{6}\) | 0.6234665 | 0.6234665 |

\(\beta_{7}\) | 2.1230663 | 2.1230663 |

\(\beta_{8}\) | 2.8035640 | 2.8035640 |

\(\beta_{9}\) | 4.4448016 | 4.4448016 |

\(\beta_{10}\) | 5.2073521 | 5.2073521 |

## Okay that was cool, but…

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

So now suppose further, for some extraneous reason, that the sum of \(\beta_2\) and \(\beta_3\) is known to be negative and but all other \(\beta\)s are positive.

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 \(\beta_2 + \beta_3\) is negative, we construct a row matrix with zeros everywhere, except in positions 2 and 3 (for \(\beta_2\) and \(\beta_3\) respectively).

```
A <- matrix(c(0, 1, 1, rep(0, 7)), nrow = 1)
colnames(A) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(A, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:10, background = "#ececec")
```

\(\beta_{1}\) | \(\beta_{2}\) | \(\beta_{3}\) | \(\beta_{4}\) | \(\beta_{5}\) | \(\beta_{6}\) | \(\beta_{7}\) | \(\beta_{8}\) | \(\beta_{9}\) | \(\beta_{10}\) |
---|---|---|---|---|---|---|---|---|---|

0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

The sum constraint is nothing but \[ A\beta <= 0 \]

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 positivity for rest of the variables, we construct a \(10\times 10\) 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, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:11, background = "#ececec")
```

\(\beta_{1}\) | \(\beta_{2}\) | \(\beta_{3}\) | \(\beta_{4}\) | \(\beta_{5}\) | \(\beta_{6}\) | \(\beta_{7}\) | \(\beta_{8}\) | \(\beta_{9}\) | \(\beta_{10}\) | |
---|---|---|---|---|---|---|---|---|---|---|

\(\beta_{1}\) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

\(\beta_{2}\) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

\(\beta_{3}\) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

\(\beta_{4}\) | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |

\(\beta_{5}\) | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |

\(\beta_{6}\) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |

\(\beta_{7}\) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |

\(\beta_{8}\) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |

\(\beta_{9}\) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |

\(\beta_{10}\) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |

The constraint for positivity is \[ B\beta >= 0 \]

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 \(\beta\).

```
m <- matrix(result$getValue(betaHat), ncol = 1)
rownames(m) <- paste0("$\\beta_{", 1:p, "}$")
knitr::kable(m, format = "html") %>%
kable_styling("striped") %>%
column_spec(1:2, background = "#ececec")
```

\(\beta_{1}\) | 0.0000000 |

\(\beta_{2}\) | -2.8446952 |

\(\beta_{3}\) | -1.7109771 |

\(\beta_{4}\) | 0.0000000 |

\(\beta_{5}\) | 0.6641308 |

\(\beta_{6}\) | 1.1781109 |

\(\beta_{7}\) | 2.3286139 |

\(\beta_{8}\) | 2.4144893 |

\(\beta_{9}\) | 4.2119052 |

\(\beta_{10}\) | 4.9483245 |

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.

## Further Reading

We hope we have whet your appetite. You may wish to read a longer introduction with more examples.

We also have a number of tutorial examples available to study and mimic.

## Session Info

`sessionInfo()`

```
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin21.6.0 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /usr/local/Cellar/openblas/0.3.21/lib/libopenblasp-r0.3.21.dylib
## LAPACK: /usr/local/Cellar/r/4.2.1_4/lib/R/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
##
## other attached packages:
## [1] nnls_1.4 CVXR_1.0-11 kableExtra_1.3.4
##
## loaded via a namespace (and not attached):
## [1] xfun_0.34 bslib_0.4.0 slam_0.1-50 lattice_0.20-45
## [5] Rmosek_10.0.25 colorspace_2.0-3 htmltools_0.5.3 viridisLite_0.4.1
## [9] yaml_2.3.6 gmp_0.6-6 rlang_1.0.6 jquerylib_0.1.4
## [13] glue_1.6.2 Rmpfr_0.8-9 Rcplex_0.3-5 bit64_4.0.5
## [17] lifecycle_1.0.3 stringr_1.4.1 munsell_0.5.0 blogdown_1.13
## [21] rvest_1.0.3 gurobi_9.5-2 codetools_0.2-18 evaluate_0.17
## [25] knitr_1.40 fastmap_1.1.0 cccp_0.2-9 highr_0.9
## [29] Rcpp_1.0.9 scales_1.2.1 cachem_1.0.6 osqp_0.6.0.5
## [33] webshot_0.5.4 jsonlite_1.8.3 systemfonts_1.0.4 bit_4.0.4
## [37] digest_0.6.30 stringi_1.7.8 bookdown_0.29 grid_4.2.1
## [41] Rglpk_0.6-4 cli_3.4.1 tools_4.2.1 magrittr_2.0.3
## [45] sass_0.4.2 rcbc_0.1.0.9001 Matrix_1.5-1 xml2_1.3.3
## [49] assertthat_0.2.1 rmarkdown_2.17 svglite_2.1.0 httr_1.4.4
## [53] rstudioapi_0.14 R6_2.5.1 compiler_4.2.1
```