文章基本信息

标题：ROI: An Extensible R Optimization Infrastructure
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作者：Stefan Theußl ; Florian Schwendinger ; Kurt Hornik 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：1-64
DOI：10.18637/jss.v094.i15
出版社：University of California, Los Angeles
摘要：Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the R optimization infrastructure ROI which provides an extensible infrastructure to model linear, quadratic, conic and general nonlinear optimization problems in a consistent way. Furthermore, the infrastructure administers many different solvers, reformulations, problem collections and functions to read and write optimization problems in various formats.
关键词：optimization;mathematical programming;linear programming;quadratic programming;convex programming;nonlinear programming;mixed integer programming;R.
其他关键词：optimization;mathematical programming;linear programming;quadratic programming;convex programming;nonlinear programming;mixed integer programming;R