# 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 conic form using graph implementations and
passed to a cone solver such
as ECOS
or SCS.

`CVXR`

paper

A preliminary draft our paper is available here.

## News

For the latest news, please see the Package Documentation.

`CVXR`

continues to be developed
on Github. The current release implements all of
the `CVXPY`

atoms except for `geo_mean`

and parameters. The latter is
not an impediment since any parameter can be replaced by a variable.

## Installing `CVXR`

`CVXR`

is installed like any other R package
from CRAN.

## Documentation

Two videos provide a good starting point:

- Anqi Fu’s talk at useR! 2018
- Anqi’s
talk
at useR! 2016 introducing
`CVXR`

.

Thanks to all attendees who participated in our tutorial at useR! 2019. The tutorial materials including exercises and solutions are available as a bookdown project. Follow the instructions in the README.

An introductory vignette is installed with the package and can be
viewed using `vignette("cvxr_intro", package = "CVXR")`

.

A large set of examples, many ported from `CVXPY`

, are available on
this site in the Examples section.

## Citing `CVXR`

If you use `CVXR`

in your work, please consider citing the paper
by Fu, Narasimhan, and Boyd (2017) below.

## Acknowledgements

We are grateful
to
Steven Diamond,
John Miller, and
Paul Kunsberg Rosenfield for their contributions to the software’s
development. In particular, we are indebted to Steven Diamond for his
work on CVXPY. Most of `CVXR`

’s code,
documentation, and examples were ported from his Python library.

## About this site

Much of the documentation on this site was modified
from `CVXPY`

in a semi-automated way.

This site was constructed using R markdown documents, the wonderful blogdown package by Yihui Xie, and the Hugo Alabaster theme. The pages are served out of the CVXR docs repository.

## References

Fu, Anqi, Balasubramanian Narasimhan, and Stephen Boyd. 2017. “CVXR: An R Package for Disciplined Convex Optimization.” https://web.stanford.edu/~boyd/papers/cvxr_paper.html.