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We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions.
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On the complexity of steepest descent, newton’s and regularized newton’s methods for nonconvex unconstrained optimization problems
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Online learning for matrix factorization and sparse coding
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Efficiency of coordinate descent methods on huge-scale optimization problems
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Accelerating stochastic gradient descent using predictive variance reduction
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On the complexity of finding first-order critical points in constrained nonlinear optimization
C. Cartis, N.I.M. Gould, and P. L. Toint · 2014
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
A. J. Defazio, F. Bach, and S. Lacoste-Julien · 2014
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Sparse modeling for image and vision processing
J. Mairal, F. Bach, and J. Ponce · 2014
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Proximal algorithms
N. Parikh and S.P. Boyd · 2014
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P. Richtarik and M. Takac · 2014
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A proximal stochastic gradient method with progressive variance reduction
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Accelerated gradient methods for nonconvex nonlinear and stochastic programming
S. Ghadimi and G. Lan · 2016
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Stochastic variance reduction for nonconvex optimization
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola · 2016
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Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization
S. J. Reddi, S. Sra, B. Poczos, and A. J. Smola · 2016
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SDCA without Duality, Regularization, and Individual Convexity
S. Shalev-Shwartz · 2016
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Tight complexity bounds for optimizing composite objectives
B. E. Woodworth and N. Srebro · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
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Generalized uniformly optimal methods for nonlinear programming
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Statistical learning with sparsity: the Lasso and generalizations
T. Hastie, R. Tibshirani, and M. Wainwright · 2015
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Adam: A method for stochastic optimization
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Accelerated proximal gradient methods for nonconvex programming
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Lower bounds for finding stationary points I
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Accelerated methods for non-convex optimization
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Efficiency of minimizing compositions of convex functions and smooth maps
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Behavior of accelerated gradient methods near critical points of nonconvex problems
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