A limited memory algorithm for bound constrained optimization RH Byrd, P Lu, J Nocedal, C Zhu SIAM Journal on scientific computing 16 (5), 1190-1208, 1995 | 4639 | 1995 |

Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization C Zhu, RH Byrd, P Lu, J Nocedal ACM Transactions on Mathematical Software (TOMS) 23 (4), 550-560, 1997 | 2427 | 1997 |

An interior point algorithm for large-scale nonlinear programming RH Byrd, ME Hribar, J Nocedal SIAM Journal on Optimization 9 (4), 877-900, 1999 | 1569 | 1999 |

A trust region method based on interior point techniques for nonlinear programming RH Byrd, JC Gilbert, J Nocedal Mathematical programming 89 (1), 149-185, 2000 | 1431 | 2000 |

Knitro: An Integrated Package for Nonlinear Optimization RH Byrd, J Nocedal, RA Waltz Large-scale nonlinear optimization, 35-59, 2006 | 915 | 2006 |

Representations of quasi-Newton matrices and their use in limited memory methods RH Byrd, J Nocedal, RB Schnabel Mathematical Programming 63 (1-3), 129-156, 1994 | 833 | 1994 |

Approximate solution of the trust region problem by minimization over two-dimensional subspaces RH Byrd, RB Schnabel, GA Shultz Mathematical programming 40 (1-3), 247-263, 1988 | 436 | 1988 |

A tool for the analysis of quasi-Newton methods with application to unconstrained minimization RH Byrd, J Nocedal SIAM Journal on Numerical Analysis 26 (3), 727-739, 1989 | 425 | 1989 |

A trust region algorithm for nonlinearly constrained optimization RH Byrd, RB Schnabel, GA Shultz SIAM Journal on Numerical Analysis 24 (5), 1152-1170, 1987 | 423 | 1987 |

A stable and efficient algorithm for nonlinear orthogonal distance regression PT Boggs, RH Byrd, RB Schnabel SIAM Journal on Scientific and Statistical Computing 8 (6), 1052-1078, 1987 | 413 | 1987 |

Global convergence of a cass of quasi-Newton methods on convex problems RH Byrd, J Nocedal, YX Yuan SIAM Journal on Numerical Analysis 24 (5), 1171-1190, 1987 | 391 | 1987 |

A family of trust-region-based algorithms for unconstrained minimization with strong global convergence properties GA Shultz, RB Schnabel, RH Byrd SIAM Journal on Numerical analysis 22 (1), 47-67, 1985 | 341 | 1985 |

A stochastic quasi-Newton method for large-scale optimization RH Byrd, SL Hansen, J Nocedal, Y Singer SIAM Journal on Optimization 26 (2), 1008-1031, 2016 | 302 | 2016 |

Algorithm 676: ODRPACK: software for weighted orthogonal distance regression PT Boggs, JR Donaldson, R Byrd, RB Schnabel ACM Transactions on Mathematical Software (TOMS) 15 (4), 348-364, 1989 | 256 | 1989 |

Sample size selection in optimization methods for machine learning RH Byrd, GM Chin, J Nocedal, Y Wu Mathematical programming 134 (1), 127-155, 2012 | 247 | 2012 |

On the use of stochastic hessian information in optimization methods for machine learning RH Byrd, GM Chin, W Neveitt, J Nocedal SIAM Journal on Optimization 21 (3), 977-995, 2011 | 193 | 2011 |

User's reference guide for odrpack version 2.01: Software for weighted orthogonal distance regression PT Boggs, PT Boggs, JE Rogers, RB Schnabel US Department of Commerce, National Institute of Standards and Technology, 1992 | 171 | 1992 |

An algorithm for nonlinear optimization using linear programming and equality constrained subproblems RH Byrd, NIM Gould, J Nocedal, RA Waltz Mathematical Programming 100 (1), 27-48, 2003 | 152 | 2003 |

An analysis of reduced Hessian methods for constrained optimization RH Byrd, J Nocedal Mathematical Programming 49 (1-3), 285-323, 1990 | 135 | 1990 |

Parallel quasi-Newton methods for unconstrained optimization RH Byrd, RB Schnabel, GA Shultz Mathematical Programming 42 (1-3), 273-306, 1988 | 116 | 1988 |