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We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011).
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Convex analysis and monotone operator theory in Hilbert spaces
H. H. Bauschke and P. L. Combettes · 2017
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Amir Beck · 2017
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Local convergence properties of Douglas–Rachford and alternating direction method of multipliers
Jingwei Liang, Jalal Fadili, and Gabriel Peyré · 2017
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J. M. Borwein and A. S. Lewis · 2006
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General resolvents for monotone operators: characterization and extension
Heinz H Bauschke, Xianfu Wang, and Liangjin Yao · 2008
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A first-order primal-dual algorithm for convex problems with applications to imaging
Antonin Chambolle and Thomas Pock · 2011
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Primal-dual first-order methods with 𝒪 ( 1 / ϵ ) \mathcal{O}(1/\epsilon) iteration-complexity for cone programming
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Partial smoothness and constant rank
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A first-order primal-dual algorithm with linesearch
Yura Malitsky and Thomas Pock · 2018
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Infeasibility detection in the alternating direction method of multipliers for convex optimization
Goran Banjac, Paul Goulart, Bartolomeo Stellato, and Stephen Boyd · 2019
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A new use of Douglas–Rachford splitting for identifying infeasible, unbounded, and pathological conic programs
Yanli Liu, Ernest K Ryu, and Wotao Yin · 2019
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The primal-dual hybrid gradient method reduces to a primal method for linearly constrained optimization problems, 2019
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On the equivalence of the primal-dual hybrid gradient method and Douglas–Rachford splitting
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