CVXR : An R Package for Disciplined Convex Optimization

作者：Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd · 发表于：Journal of Statistical Software · 年份：2020 · DOI：10.18637/jss.v094.i14 · 被引用次数：118 · 研究领域：Advanced Optimization Algorithms Research、Advanced Multi-Objective Optimization Algorithms、Constraint Satisfaction and Optimization

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.