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The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a more general feasible region.
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Yurii Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems , SIAM Journal on Optimization 22
2012
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2012
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Martin Jaggi, Revisiting Frank-Wolfe: Projection-free sparse convex optimization , Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 427–435
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Rie Johnson and Tong Zhang, Accelerating stochastic gradient descent using predictive variance reduction , Advances in neural information processing systems, 2013, pp. 315–323
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Robert Freund and Paul Grigas, New analysis and results for the Frank–Wolfe method , Mathematical Programming 155
2016
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Elad Hazan and Haipeng Luo, Variance-reduced and projection-free stochastic optimization , International Conference on Machine Learning, 2016, pp. 1263–1271
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Guanghui Lan and Yi Zhou, Conditional gradient sliding for convex optimization , SIAM Journal on Optimization 26
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Shai Shalev-Shwartz, Sdca without duality, regularization, and individual convexity , International Conference on Machine Learning, 2016, pp. 747–754
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Zeyuan Allen-Zhu, Katyusha: The first direct acceleration of stochastic gradient methods , Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, ACM, 2017, pp. 1200–1205
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2014
Cited alongside, same era.
Yu Nesterov, Subgradient methods for huge-scale optimization problems , Mathematical Programming 146
2014
Cited alongside, same era.
Peter Richtarik and Martin Takac, Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function , Mathematical Programming 144
2014
Cited alongside, same era.
Francis Bach, Duality between subgradient and conditional gradient methods , SIAM Journal on Optimization 25
2015
Cited alongside, same era.
Zaid Harchaoui, Anatoli Juditsky, and Arkadi Nemirovski, Conditional gradient algorithms for norm-regularized smooth convex optimization , Mathematical Programming 152
2015
Cited alongside, same era.
Qihang Lin, Zhaosong Lu, and Lin Xiao, An accelerated randomized proximal coordinate gradient method and its application to regularized empirical risk minimization , SIAM Journal on Optimization 25
2015
Cited alongside, same era.
Zhaosong Lu and Lin Xiao, On the complexity analysis of randomized block-coordinate descent methods , Mathematical Programming 152
2015
Cited alongside, same era.
Julien Mairal, Incremental majorization-minimization optimization with application to large-scale machine learning , SIAM Journal on Optimization 25
2015
Cited alongside, same era.
2017
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Robert Freund, Paul Grigas, and Rahul Mazumder, An extended Frank–Wolfe method with “in-face” directions, and its application to low-rank matrix completion , SIAM Journal on Optimization 27
2017
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2017
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Mark Schmidt, Nicolas Le Roux, and Francis Bach, Minimizing finite sums with the stochastic average gradient , Mathematical Programming 162
2017
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Quang Van Nguyen, Forward-backward splitting with Bregman distances , Vietnam Journal of Mathematics 45
2017
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Yaoliang Yu, Xinhua Zhang, and Dale Schuurmans, Generalized conditional gradient for sparse estimation , The Journal of Machine Learning Research 18
2017
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2017
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2018
Closest in time.
Haihao Lu, Robert Freund, and Yurii Nesterov, Relatively smooth convex optimization by first-order methods, and applications , SIAM Journal on Optimization 28
2018
Closest in time.