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We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization.
Proximal splitting algorithms for convex optimization: A tour of recent advances, with new twists
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Every convex function is locally Lipschitz
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Convex Optimization in Signal Processing and Communications
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Proximal splitting methods in signal processing
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F. Bach, R. Jenatton, J. Mairal, and G. Obozinski · 2012
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Primal–dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators
P. L. Combettes and J.-C. Pesquet · 2012
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A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
P. Chen, J. Huang, and X. Zhang · 2013
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A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
L. Condat · 2013
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B. C. Vũ · 2013
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Recent developments on primal–dual splitting methods with applications to convex minimization
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V. Cevher, S. Becker, and M. Schmidt · 2014
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A forward–backward view of some primal–dual optimization methods in image recovery
P. L. Combettes, L. Condat, J.-C. Pesquet, and B. C. Vũ · 2014
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
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First-Order Methods in Optimization
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