# Solver Interfaces in CVXR

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## Introduction

In our previous blog

post, we

introduced `CVXR`

, an R package for disciplined convex

optimization. The package allows one to describe an optimization

problem with Disciplined Convex Programming

rules using high level mathematical syntax. Passing this problem

definition along (with a list of constraints, if any) to the `solve`

function transforms it into a form that can be handed off to

a solver. The default installation of `CVXR`

comes with two (imported)

open source solvers:

- ECOS and its mixed integer

cousin`ECOS_BB`

via the CRAN package

ECOSolveR - SCS via the CRAN package

scs.

`CVXR`

(version 0.99) can also make use of several other open source

solvers implemented in R packages:

- The linear and mixed integer programming package

`lpSolve`

via the

`lpSolveAPI`

package - The linear and mixed integer programming package
`GLPK`

via the

`Rglpk`

package.

## About Solvers

The real work of finding a solution is done by solvers, and writing

good solvers is hard work. Furthermore, some solvers work particularly

well for certain types of problems (linear programs, quadratic

programs, etc.). Not surprisingly, there are commercial vendors who

have solvers that are designed for performance and scale. Two

well-known solvers are MOSEK and

GUROBI. R packages for these solvers are

also provided, but they require the problem data to be constructed in

a specific form. This necessitates a bit of work in the current version of

`CVXR`

and is certainly something we plan to include in future versions.

However, it is also true that these commercial solvers expose a much

richer API to Python programmers than to R programmers. How, then, do we

interface such solvers with R as quickly as possible, at least

in the short term?

## Reticulate to the Rescue

The current version of `CVXR`

exploits the

`reticulate`

package

for commercial solvers such as MOSEK and GUROBI. We took the Python solver interfaces in `CVXPY`

version

0.4.11, edited them

suitably to make them self-contained, and hooked them up to `reticulate`

.

This means that one needs two prerequisites to use these commercial solvers in the current version of `CVXR`

:

- A Python installation
- The
`reticulate`

R

package.

## Installing MOSEK/GUROBI

Both MOSEK and

GUROBI provide academic versions

(registration required) free of charge. For example,

Anaconda users can install MOSEK with the command:

conda install -c mosek mosek

Others can use the `pip`

command:

pip install -f https://download.mosek.com/stable/wheel/index.html Mosek

GUROBI is handled in a similar fashion. The solvers must be activated using a

license provided by the vendor.

Once activated, one can check that `CVXR`

recognizes the solver;

`installed_solvers()`

should list them.

> installed_solvers() [1] "ECOS" "ECOS_BB" "SCS" "MOSEK" "LPSOLVE" "GLPK" "GUROBI"

## Further information

More information on these solvers, along with a number of tutorial examples are

available on the CVXR site. If you are

attending useR! 2018, you can catch

Anqi’s `CVXR`

talk on Friday, July 13.

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