Introduction to derivative-free optimization AR Conn, K Scheinberg, LN Vicente Siam, 2009 | 1330 | 2009 |

Efficient SVM training using low-rank kernel representations S Fine, K Scheinberg Journal of Machine Learning Research 2 (Dec), 243-264, 2001 | 680 | 2001 |

Recent progress in unconstrained nonlinear optimization without derivatives AR Conn, K Scheinberg, PL Toint Mathematical programming 79 (1-3), 397, 1997 | 313 | 1997 |

Fast alternating linearization methods for minimizing the sum of two convex functions D Goldfarb, S Ma, K Scheinberg Mathematical Programming 141 (1-2), 349-382, 2013 | 233 | 2013 |

On the convergence of derivative-free methods for unconstrained optimization AR Conn, K Scheinberg, PL Toint Approximation theory and optimization: tributes to MJD Powell, 83-108, 1997 | 218 | 1997 |

Global convergence of general derivative-free trust-region algorithms to first-and second-order critical points AR Conn, K Scheinberg, LN Vicente SIAM Journal on Optimization 20 (1), 387-415, 2009 | 189 | 2009 |

Sparse inverse covariance selection via alternating linearization methods K Scheinberg, S Ma, D Goldfarb Advances in neural information processing systems, 2101-2109, 2010 | 170 | 2010 |

Efficient block-coordinate descent algorithms for the group lasso Z Qin, K Scheinberg, D Goldfarb Mathematical Programming Computation 5 (2), 143-169, 2013 | 155 | 2013 |

A derivative free optimization algorithm in practice A Conn, K Scheinberg, P Toint 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and c, 1998 | 143 | 1998 |

Geometry of interpolation sets in derivative free optimization AR Conn, K Scheinberg, LN Vicente Mathematical programming 111 (1-2), 141-172, 2008 | 119 | 2008 |

Interior point trajectories in semidefinite programming D Goldfarb, K Scheinberg SIAM Journal on Optimization 8 (4), 871-886, 1998 | 105 | 1998 |

An efficient implementation of an active set method for SVMs K Scheinberg Journal of Machine Learning Research 7 (Oct), 2237-2257, 2006 | 102 | 2006 |

SARAH: A novel method for machine learning problems using stochastic recursive gradient LM Nguyen, J Liu, K Scheinberg, M Takáč Proceedings of the 34th International Conference on Machine Learning-Volume c, 2017 | 88 | 2017 |

Self-correcting geometry in model-based algorithms for derivative-free unconstrained optimization K Scheinberg, PL Toint SIAM Journal on Optimization 20 (6), 3512-3532, 2010 | 75 | 2010 |

Geometry of sample sets in derivative-free optimization: polynomial regression and underdetermined interpolation AR Conn, K Scheinberg, LN Vicente IMA journal of numerical analysis 28 (4), 721-748, 2008 | 73 | 2008 |

A derivative-free algorithm for least-squares minimization H Zhang, AR Conn, K Scheinberg SIAM Journal on Optimization 20 (6), 3555-3576, 2010 | 72 | 2010 |

Duality and optimality conditions A Shapiro, K Scheinberg Handbook of semidefinite programming, 67-110, 2000 | 62 | 2000 |

Block coordinate descent methods for semidefinite programming Z Wen, D Goldfarb, K Scheinberg Handbook on semidefinite, conic and polynomial optimization, 533-564, 2012 | 60 | 2012 |

Intensive optimization of masks and sources for 22nm lithography AE Rosenbluth, DO Melville, K Tian, S Bagheri, J Tirapu-Azpiroz, K Lai, ... Optical Microlithography XXII 7274, 727409, 2009 | 54 | 2009 |

Convergence of trust-region methods based on probabilistic models AS Bandeira, K Scheinberg, LN Vicente SIAM Journal on Optimization 24 (3), 1238-1264, 2014 | 52 | 2014 |