Disciplined Convex Optimization

An object-oriented modeling language for disciplined convex programming (DCP). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution.

CVXR 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 includes several open source solvers in addition to the default ECOS and SCS. Recent versions (0.97+) also include support for commercial solvers such as MOSEK and GUROBI.

Installation

This package is now released on CRAN, so you can install the current released version as you would any other package for R, version 3.4 and higher. (CVXR is known to work with earlier versions of R too, but we don’t check our releases against older versions of R.)

install.packages('CVXR', repos = "https://CRAN.R-project.org")

Development versions can be installed from the Github repository assuming you have the development tools for R available, including the C compilers etc. Execute:

library(devtools)
install_github("anqif/CVXR")

Tutorial

A number of tutorial examples are available on the CVXR website.