Jonathan Eckstein
Jonathan Eckstein
Professor of Management Science and Information Systems, Rutgers University
Verified email at - Homepage
Cited by
Cited by
Distributed optimization and statistical learning via the alternating direction method of multipliers
S Boyd, N Parikh, E Chu
Now Publishers Inc, 2011
On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators
J Eckstein, DP Bertsekas
Mathematical Programming 55 (1-3), 293-318, 1992
Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming
J Eckstein
Mathematics of Operations Research 18 (1), 202-226, 1993
Splitting methods for monotone operators with applications to parallel optimization
J Eckstein
Massachusetts Institute of Technology, 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
Approximate iterations in Bregman-function-based proximal algorithms
J Eckstein
Mathematical programming 83 (1-3), 113-123, 1998
Parallel alternating direction multiplier decomposition of convex programs
J Eckstein
Journal of Optimization Theory and Applications 80 (1), 39-62, 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
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
Some reformulations and applications of the alternating direction method of multipliers
J Eckstein, M Fukushima
Large scale optimization, 115-134, 1994
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
Some saddle-function splitting methods for convex programming
J Eckstein
Optimization Methods and Software 4 (1), 75-83, 1994
Stochastic dedication: Designing fixed income portfolios using massively parallel Benders decomposition
RS Hiller, J Eckstein
Management Science 39 (11), 1422-1438, 1993
Dual coordinate step methods for linear network flow problems
DP Bertsekas, J Eckstein
Mathematical Programming 42 (1-3), 203-243, 1988
Distributed asynchronous relaxation methods for linear network flow problems
DP Bertsekas, J Eckstein
IFAC Proceedings Volumes 20 (5), 103-114, 1987
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
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
A family of projective splitting methods for the sum of two maximal monotone operators
J Eckstein, BF Svaiter
Mathematical Programming 111 (1-2), 173-199, 2008
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
Managing periodically updated data in relational databases: A stochastic modeling approach
A Gal, J Eckstein
Journal of the ACM (JACM) 48 (6), 1141-1183, 2001
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