Introduction : Optimization and machine learning / S. Sra, S. Nowozin, and S.J. Wright -- Convex optimization with sparsity-inducing norms / F. Bach, R. Jenatton, J. Mairal, and G. Obozinski -- Interior-point methods for large-scale cone programming / M. Andersen, J. Dahl, Z. Liu, and L. Vanderberghe -- Incremental gradient, subgradient, and proximal methods for convex optimization : a survey / D. P. Bertsekas -- First-order methods for nonsmooth convex large-scale optimization, I : general purpose methods / A. Juditsky and A. Nemirovski -- First-order methods for nonsmooth convex large-scale optimization, II : utilizing problem's structure / A. Juditsky and A. Nemirovski -- Cutting-plane methods in machine learning / V. Franc, S. Sonnenburg, and T. Werner -- Introduction to dual decomposition for inference / D. Sontag, A. Globerson, and T. Jaakkola -- Augmented Lagrangian methods for learning, selecting, and combining features / R. Tomioka, T. Suzuki, and M. Sugiyama -- The convex optimization approach to regret minimization / E. Hazan -- Projected Newton-type methods in machine learning / M. Schmidt, D. Kim, and S. Sra -- Interior-point methods in machine learning / J. Gondzio -- The tradeoffs of large-scale learning / L. Bottou and O. Bousquet -- Robust optimization in machine learning / C. Caramanis, S. Mannor, and H. Xu -- Improving first and second-order methods by modeling uncertainty / N. Le Roux, Y. Bengio, and A. Fitzgibbon -- Bandit view on noisy optimization / J.-Y. Audibert, S. Bubeck, and R. Munos -- Optimization methods for sparse inverse covariance selection / K. Scheinberg and S. Ma -- A pathwise algorithm for covariance selection / V. Krishnamurthy, S. D. Ahipasaoglu, and A. d'Aspremont.

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