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We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.
A stochastic approximation method
1951
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A convergence theorem for non-negative almost supermartingales and some applications
1971
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Non-negative matrix factorization with sparseness constraints
2004
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A direct formulation for sparse pca using semidefinite programming
2005
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Sparse principal component analysis
2006
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On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
2007
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The Łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems
2007
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Spectral regression for efficient regularized subspace learning
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Learning a spatially smooth subspace for face recognition
2007
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Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka-Lojasiewicz inequality
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Characterizations of Lojasiewicz inequalities: subgradient flows, talweg, convexity
2010
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Large-scale machine learning with stochastic gradient descent
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Robust principal component analysis?
2011
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Non-asymptotic analysis of stochastic approximation algorithms for machine learning
2011
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Accelerating stochastic gradient descent using predictive variance reduction
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Proximal alternating linearised minimization for nonconvex and nonsmooth problems
2014
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
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A proximal stochastic gradient method with progressive variance reduction
2014
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Mini-batch semi-stochastic gradient descent in the proximal setting
2015
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SARAH: A novel method for machine learning problems using stochastic recursive gradient
2017
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Minimizing finite sums with the stochastic average gradient
2017
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Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications
2018
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Calculus of the exponent of kurdyka–Łojasiewicz inequality and its applications to linear convergence of first-order methods
2018
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SpiderBoost: A class of faster variance-reduced algorithms for nonconvex optimization
2018
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Trimmed statistical estimation via variance reduction
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