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Composite minimization is a powerful framework in large-scale convex optimization, based on decoupling of the objective function into terms with structurally different properties and allowing for more flexible algorithmic design.
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Yurii Nesterov · 2012
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Nathan Srebro and Karthik Sridharan · 2012
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Marco Cuturi · 2013
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Optimal affine-invariant smooth minimization algorithms
Alexandre d’Aspremont, Cristóbal Guzmán, and Martin Jaggi · 2018
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Jelena Diakonikolas and Lorenzo Orecchia · 2018
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Error bounds, quadratic growth, and linear convergence of proximal methods
Dmitriy Drusvyatskiy and Adrian S Lewis · 2018
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Computational optimal transport: Complexity by accelerated gradient descent is better than by sinkhorn’s algorithm
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Relatively smooth convex optimization by first-order methods, and applications
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Yu Nesterov · 2013
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Yurii Nesterov and Arkadi Nemirovski · 2013
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First-order methods of smooth convex optimization with inexact oracle
Olivier Devolder, François Glineur, and Yurii Nesterov · 2014
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Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
Anatoli Juditsky and Yuri Nesterov · 2014
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Fast first-order methods for composite convex optimization with backtracking
Katya Scheinberg, Donald Goldfarb, and Xi Bai · 2014
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On lower complexity bounds for large-scale smooth convex optimization
Cristóbal Guzmán and Arkadi Nemirovski · 2015
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Haihao Lu, Robert M Freund, and Yurii Nesterov · 2018
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