Distributed optimization and statistical learning via the alternating direction method of multipliers S Boyd, N Parikh, E Chu, B Peleato, J Eckstein Foundations and TrendsŪ in Machine learning 3 (1), 1-122, 2011 | 22414 | 2011 |

On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators J Eckstein, DP Bertsekas Mathematical programming 55, 293-318, 1992 | 3253 | 1992 |

Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming J Eckstein Mathematics of Operations Research 18 (1), 202-226, 1993 | 445 | 1993 |

Splitting methods for monotone operators with applications to parallel optimization J Eckstein Massachusetts Institute of Technology, 1989 | 408 | 1989 |

Augmented Lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results J Eckstein, W Yao RUTCOR Research Reports 32 (3), 44, 2012 | 306 | 2012 |

Understanding the convergence of the alternating direction method of multipliers: Theoretical and computational perspectives J Eckstein, W Yao Pac. J. Optim. 11 (4), 619-644, 2015 | 227 | 2015 |

Approximate iterations in Bregman-function-based proximal algorithms J Eckstein Mathematical programming 83, 113-123, 1998 | 212 | 1998 |

Parallel alternating direction multiplier decomposition of convex programs J Eckstein Journal of Optimization Theory and Applications 80 (1), 39-62, 1994 | 194 | 1994 |

Some reformulations and applications of the alternating direction method of multipliers J Eckstein, M Fukushima Large Scale Optimization: State of the Art, 115-134, 1994 | 170 | 1994 |

PICO: An object-oriented framework for parallel branch and bound J Eckstein, CA Phillips, WE Hart Studies in Computational Mathematics 8, 219-265, 2001 | 161 | 2001 |

Some saddle-function splitting methods for convex programming J Eckstein Optimization Methods and Software 4 (1), 75-83, 1994 | 156 | 1994 |

Dual coordinate step methods for linear network flow problems DP Bertsekas, J Eckstein Mathematical Programming 42 (1), 203-243, 1988 | 141 | 1988 |

Parallel branch-and-bound algorithms for general mixed integer programming on the CM-5 J Eckstein SIAM journal on optimization 4 (4), 794-814, 1994 | 133 | 1994 |

Stochastic dedication: Designing fixed income portfolios using massively parallel Benders decomposition RS Hiller, J Eckstein Management Science 39 (11), 1422-1438, 1993 | 131 | 1993 |

Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3 (1): 1–122 S Boyd, N Parikh, E Chu, B Peleato, J Eckstein doi. org/10.1561/2200000016, 2011 | 128 | 2011 |

Distributed asynchronous relaxation methods for linear network flow problems DP Bertsekas, J Eckstein IFAC Proceedings Volumes 20 (5), 103-114, 1987 | 128 | 1987 |

A family of projective splitting methods for the sum of two maximal monotone operators J Eckstein, BF Svaiter Mathematical Programming 111, 173-199, 2008 | 121 | 2008 |

General projective splitting methods for sums of maximal monotone operators J Eckstein, BF Svaiter SIAM Journal on Control and Optimization 48 (2), 787-811, 2009 | 116 | 2009 |

Operator-splitting methods for monotone affine variational inequalities, with a parallel application to optimal control J Eckstein, MC Ferris INFORMS Journal on Computing 10 (2), 218-235, 1998 | 110 | 1998 |

The maximum box problem and its application to data analysis J Eckstein, PL Hammer, Y Liu, M Nediak, B Simeone Computational Optimization and Applications 23 (3), 285-298, 2002 | 101 | 2002 |