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Mathematics > Optimization and Control

arXiv:1812.00628 (math)
[Submitted on 3 Dec 2018 (v1), last revised 26 Sep 2019 (this version, v2)]

Title:A generic coordinate descent solver for nonsmooth convex optimization

Authors:Olivier Fercoq (LTCI)
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Abstract:We present a generic coordinate descent solver for the minimization of a nonsmooth 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 programs.
Subjects: Optimization and Control (math.OC)
Cite as: arXiv:1812.00628 [math.OC]
  (or arXiv:1812.00628v2 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.1812.00628
Journal reference: Optimization Methods and Software, Taylor \& Francis, 2019, pp.1-21. \&\#x27E8;10.1080/10556788.2019.1658758\&\#x27E9

Submission history

From: Olivier Fercoq [view email] [via CCSD proxy]
[v1] Mon, 3 Dec 2018 09:40:01 UTC (21 KB)
[v2] Thu, 26 Sep 2019 14:11:58 UTC (85 KB)
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