Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition H Karimi, J Nutini, M Schmidt Joint European conference on machine learning and knowledge discovery in …, 2016 | 786 | 2016 |

Coordinate descent converges faster with the gauss-southwell rule than random selection J Nutini, M Schmidt, I Laradji, M Friedlander, H Koepke International Conference on Machine Learning, 1632-1641, 2015 | 214 | 2015 |

A survey of non-gradient optimization methods in structural engineering W Hare, J Nutini, S Tesfamariam Advances in Engineering Software 59, 19-28, 2013 | 179 | 2013 |

Convergence rates for greedy Kaczmarz algorithms, and faster randomized Kaczmarz rules using the orthogonality graph J Nutini, B Sepehry, I Laradji, M Schmidt, H Koepke, A Virani arXiv preprint arXiv:1612.07838, 2016 | 57 | 2016 |

A derivative-free approximate gradient sampling algorithm for finite minimax problems W Hare, J Nutini Computational Optimization and Applications 56 (1), 1-38, 2013 | 57 | 2013 |

Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence J Nutini, I Laradji, M Schmidt arXiv preprint arXiv:1712.08859, 2017 | 53 | 2017 |

“Active-set complexity” of proximal gradient: How long does it take to find the sparsity pattern? J Nutini, M Schmidt, W Hare Optimization Letters 13 (4), 645-655, 2019 | 38 | 2019 |

Are we there yet? manifold identification of gradient-related proximal methods Y Sun, H Jeong, J Nutini, M Schmidt The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 28 | 2019 |

Optimizing damper connectors for adjacent buildings K Bigdeli, W Hare, J Nutini, S Tesfamariam Optimization and Engineering 17 (1), 47-75, 2016 | 25 | 2016 |

Convergence rates for greedy Kaczmarz algorithms J Nutini, B Sepehry, A Virani, I Laradji, M Schmidt, H Koepke Conference on Uncertainty in Artificial Intelligence, 2016 | 12 | 2016 |

Greed is good: greedy optimization methods for large-scale structured problems J Nutini University of British Columbia, 2018 | 11 | 2018 |

Optimal design of damper connectors for adjacent buildings K Bigdeli, W Hare, J Nutini, S Tesfamariam Comput Struct, submitted for publication, 2013 | 2 | 2013 |

Greed is Good J Nutini PhD thesis, University of British Columbia, 2018 | 1 | 2018 |

Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph J Nutini, M Schmidt, B Sepehry, H Koepke, I Laradji, A Virani J. Fourier Anal. Appl 15 (2), 262-278, 2009 | 1 | 2009 |

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence J Nutini, I Laradji, M Schmidt Journal of Machine Learning Research 23 (131), 1-74, 2022 | | 2022 |

Putting the curvature back into sparse solvers J Nutini | | 2013 |

A derivative-free approximate gradient sampling algorithm for finite minimax problems JA Nutini University of British Columbia, 2012 | | 2012 |

Are we there yet? Manifold identification of gradient-related proximal methods Download PDF Y Sun, H Jeong, J Nutini, M Schmidt | | |

“Active-set complexity” of proximal gradient J Nutini, M Schmidt, W Hare | | |

Graphical Newton for Huge-Block Coordinate Descent on Sparse Graphs I Laradji, J Nutini, M Schmidt | | |