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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 OSQP, ECOS and SCS. Recent (1.x+) versions also include support for commercial solvers such as MOSEK, GUROBI and CPLEX.
For details and examples, we refer you to Fu, Narasimhan, Boyd
(2020). If you use CVXR in your work, please cite this reference. (The R
command citation("CVXR", bibtex = TRUE) will also give you
a bibtex-formatted entry.)
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("cvxgrp/CVXR")A number of tutorial examples are available on the CVXR website along with links to our useR! 2019 short-course.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.