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We present new results for the Frank-Wolfe method (also known as the conditional gradient method).
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O. Devolder, F. Glineur, and Y.E. Nesterov, First-order methods of smooth convex optimization with inexact oracle , Tech. report, CORE, Louvain-la-Neuve, Belgium, 2013
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Z. Harchaoui, A. Juditsky, and A. Nemirovski, Conditional gradient algorithms for norm-regularized smooth convex optimization , Technical R
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M. 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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Y.E. Nesterov, Primal-dual subgradient methods for convex problems , Mathematical Programming 120
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M. Dudík, Z. Harchaoui, and J. Malick, Lifted coordinate descent for learning with trace-norm regularization , AISTATS (2012), 327–336
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S. Lacoste-Julien, M. Jaggi, M. Schmidt, and P. Pletscher, Block-coordinate frank-wolfe optimization for structural svms , Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013
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G. Lan, The complexity of large-scale convex programming under a linear optimization oracle , Tech. report, Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, 2013
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R.M. Freund, P. Grigas, and R. Mazumder, A first-order view of boosting methods, with implications for regularization and computational guarantees for loss minimization algorithms , Tech. report, MIT Operations Research Center, in preparation, 2014
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