A generic coordinate descent solver for non-smooth convex optimisation. (2nd November 2021)

• Record Type: Journal Article
• Authors: Fercoq, Olivier
• Is Part Of: Optimization methods and software. Volume 36:Number 6(2021)
• Page Start: 1202
• Page End: 1222
• Publication Date: 2021-11-02
• Subjects: Coordinate descent -- convex optimisation -- generic solver -- efficient implementation; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2019.1658758
• Languages: English
• ISSNs: 1055-6788

Abstract

ABSTRACT: We present a generic coordinate descent solver for the minimisation of a non-smooth convex objective with structure. The method can deal in particular with problems with linear constraints. The implementation makes use of efficient residual updates and automatically determines which dual variables should be duplicated. A list of basic functional atoms is pre-compiled for efficiency and a modelling language in Python allows the user to combine them at run time. So, the algorithm can be used to solve a large variety of problems including Lasso, sparse multinomial logistic regression, linear and quadratic programmes.