Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms

M. Marques Alves (), Jonathan Eckstein (), Marina Geremia () and Jefferson G. Melo ()
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M. Marques Alves: Universidade Federal de Santa Catarina
Jonathan Eckstein: Rutgers Business School Newark and New Brunswick
Marina Geremia: Universidade Federal de Santa Catarina
Jefferson G. Melo: Universidade Federal de Goiás

Computational Optimization and Applications, 2020, vol. 75, issue 2, No 3, 389-422

Abstract: Abstract This paper derives new inexact variants of the Douglas-Rachford splitting method for maximal monotone operators and the alternating direction method of multipliers (ADMM) for convex optimization. The analysis is based on a new inexact version of the proximal point algorithm that includes both an inertial step and overrelaxation. We apply our new inexact ADMM method to LASSO and logistic regression problems and obtain somewhat better computational performance than earlier inexact ADMM methods.

Keywords: Convex optimization; ADMM; Inertial algorithms; Douglas-Rachford splitting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10589-019-00165-y

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