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Installation Guide

C++ Interface

Compile C++ sources and run tests

To compile the C++ code you need the lp_solve library. For example, for Unix/Linux you need liblpsolve55.so. This is available from the library's webpage as well as a package in several linux distributions e.g. debian sudo apt-get install lp-solve.

You have to specify the path to liblpsolve55.so/dll/dylib, by running, in folder test:

mkdir -p test/build && cd test/build
cmake -DLP_SOLVE=_PATH_TO_LIB_FILE_ ..
make

For example: -DLP_SOLVE=/usr/lib/lpsolve/liblpsolve55.so

In WSL (Windows Subsystem Linux), you can run the following command to install libc6-dev-i386. This will be required for ieeefp.h which is used by qd library,

sudo apt-get install libc6-dev-i386

Also to install mkl related dependencies, run the following,

wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update
sudo apt-get install intel-mkl-2020.4-912
sudo sh -c "echo /opt/intel/mkl/lib/intel64 > /etc/ld.so.conf.d/intel-mkl.conf"
sudo ldconfig
export CPLUS_INCLUDE_PATH="/opt/intel/mkl/include:$CPLUS_INCLUDE_PATH"

You can run the tests by cmake test or ctest -jK where K the number of CPU threads. By adding the option --verbose to ctest you get more information about the tests, e.g. time per test, volume computed and the name of the polytope or convex body.

Development environment from Docker container

Optionally, it is possible to setup a docker contaner with development environment. To get started with docker, see here. Below, here is how a Dockerfile can be written

FROM ubuntu:18.04

RUN apt-get update && apt-get install -y g++ cmake lp-solve && \
    rm -rf /var/lib/apt/lists/*

The user should create a file Dockerfile.dev with above content inside a temporary folder (e.g. docker) then create an image and run a container:

# build docker image
cd docker
docker build -t volesti:dev -f Dockerfile.dev .
# check built image
docker images | grep volesti
# run a container in an interactive mode from volesti source folder
docker run -it -v $PWD:/volesti -w /volesti --name=volesti-dev volesti:dev /bin/bash

R Interface

Install Rcpp package

  1. Install package-dependencies: Rcpp, RcppEigen, BH.

  2. Then use devtools package to install volesti Rcpp package. From terminal go to folder /root/R-proj and run in terminal:

    Rscript -e 'Rcpp::compileAttributes()'
    R CMD INSTALL --no-multiarch --with-keep.source .
  1. You can use Rstudio as well to open volesti.Rproj and then click build source Package and then Install and Restart in Build at the menu bar.

Generate CRAN version

To generate the CRAN version of the R package follow the instructions below:

  1. From the command line navigate to folder /cran_gen. Then Run:
    source('genCRANpkg.R')
  1. Open genCRANpkg.R script with Rstudio and run it.

Run volesti from R

  • The main function is volume(). It can be used to approximate the volume of a convex polytope given as a set of linear inequalities or a set of vertices (d-dimensional points) or as a Minkowski sum of segments (zonotope). There are three algorithms that can be used (SequenceOfBalls, CoolingGaussian and CoolingBalls).
  • The function sample_points() can be used to sample points from a convex polytope with uniform or spherical gaussian target distribution.
  • The function round_polytope() can be used to round a convex polytope.
  • The function rand_rotate() can be used to apply a random rotation to a convex polytope.

For more details you can read the documentation in folder /inst/doc.

Python Interface

A python interface is available from the package dingo.