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We develop a novel framework to study smooth and strongly convex optimization algorithms, both deterministic and stochastic.
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Bounds for eigenvalues of matrix polynomials
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Stochastic approximation and recursive algorithms and applications , volume 35
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Yurii Nesterov · 2004
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Bounds for the extreme eigenvalues using the trace and determinant
Qin Zhong and Ting-Zhu Huang · 2008
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Estimate sequence methods: extensions and approximations
Michel Baes · 2009
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Matrix polynomials , volume 58
Israel Gohberg, Pnesteeter Lancaster, and Leiba Rodman · 2009
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A stochastic gradient method with an exponential convergence rate for finite training sets
Nicolas Le Roux, Mark Schmidt, and Francis Bach · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
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Efficient methods in convex programming
Arkadi Nemirovski · 2005
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Introduction to stochastic search and optimization: estimation, simulation, and control , volume 65
James C Spall · 2005
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Improving bounds for eigenvalues of complex matrices using traces
Ting-Zhu Huang and Lin Wang · 2007
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On accelerated proximal gradient methods for convex-concave optimization. submitted to siam j
Paul Tseng · 2008
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang
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Stochastic dual coordinate ascent methods for regularized loss
Shai Shalev-Shwartz and Tong Zhang
Cited in the paper.
Rie Johnson and Tong Zhang · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Zeyuan Allen-Zhu and Lorenzo Orecchia · 2014
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Analysis and design of optimization algorithms via integral quadratic constraints
Laurent Lessard, Benjamin Recht, and Andrew Packard · 2014
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