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Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions.
On the numerical solution of heat conduction problems in two and three space variables
J. Douglas and HH Rachford · 1956
Earlier work this paper cites.
Splitting algorithms for the sum of two nonlinear operators
P.L. Lions and B. Mercier · 1979
Earlier work this paper cites.
Chapter ix applications of the method of multipliers to variational inequalities
D. Gabay · 1983
Earlier work this paper cites.
Applications of a splitting algorithm to decomposition in convex programming and variational inequalities
P. Tseng · 1991
Earlier work this paper cites.
On the douglas—rachford splitting method and the proximal point algorithm for maximal monotone operators
J. Eckstein and D.P. Bertsekas · 1992
Cited alongside, same era.
Solving monotone inclusions via compositions of nonexpansive averaged operators
P.L. Combettes · 2004
Cited alongside, same era.
Signal recovery by proximal forward-backward splitting
P.L. Combettes and V.R. Wajs · 2005
Cited alongside, same era.
A douglas–rachford splitting approach to nonsmooth convex variational signal recovery
P.L. Combettes and J.C. Pesquet · 2007
Cited alongside, same era.
Cvx: Matlab software for disciplined convex programming
Michael Grant and Stephen Boyd
Cited in the paper.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
Later among the works it cites.
Proximal splitting methods in signal processing
P.L. Combettes and J.C. Pesquet · 2011
Later among the works it cites.
The Large Time-Frequency Analysis Toolbox 2.0
Zdeněk Průša, Peter L. Søndergaard, Nicki Holighaus, Christoph Wiesmeyr, and Peter Balazs · 2014
Closest in time.
Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems
Nikos Komodakis and Jean-Christophe Pesquet · 2015
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