文章基本信息

标题：CVXR: An R Package for Disciplined Convex Optimization
本地全文：下载
作者：Anqi Fu ; Balasubramanian Narasimhan ; Stephen Boyd 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：94
期号：1
页码：1-34
DOI：10.18637/jss.v094.i14
出版社：University of California, Los Angeles
摘要：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 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. We demonstrate CVXR's modeling framework with several applications.
关键词：convex optimization;disciplined convex optimization;optimization;regression;penalized regression;isotonic regression;R package CVXR.
其他关键词：convex optimization;disciplined convex optimization;optimization;regression;penalized regression;isotonic regression;R package CVXR