World's Best Scientists 2026 revealed!

Overview

Jonathan Eckstein is affiliated with Rutgers, The State University of New Jersey in the United States. Their research spans multiple disciplines including engineering, computer science, and mathematics, with particular emphasis on computational mechanics, computational theory and mathematics, and numerical analysis.

Their work covers a range of specialized topics such as sparse and compressive sensing techniques, advanced optimization algorithms research, and optimization and variational analysis. Additional areas of focus include stochastic gradient optimization techniques, prosthetics and rehabilitation robotics, stroke rehabilitation and recovery, and studies related to muscle activation and electromyography.

Jonathan Eckstein has contributed to the following recent papers:

  • Deriving solution value bounds from the ADMM, 2020, Optimization Letters
  • Projective splitting with forward steps, 2020, Mathematical Programming
  • Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms, 2020, Computational Optimization and Applications
  • Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation, 2023, Operations Research
  • Performance assessment of a green hydrogen-based household energy system supported by a battery storage at different resolutions of the electrical load profile, 2025, International Journal of Hydrogen Energy

Frequent co-authors collaborating with Jonathan Eckstein include:

  • Patrick R. Johnstone
  • Thomas R. Flynn
  • Shinjae Yoo
  • Birgit Graf
  • M. Marques Alves

The main publication venues for Jonathan Eckstein's work include:

  • Computational Optimization and Applications
  • arXiv (Cornell University)
  • Mathematical Programming
  • Operations Research
  • International Journal of Hydrogen Energy

Best Publications

  • Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

    Stephen Boyd;Neal Parikh;Eric Chu;Borja Peleato

  • On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators

    Jonathan Eckstein;Dimitri P. Bertsekas

  • Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming

    Jonathan Eckstein

  • Splitting methods for monotone operators with applications to parallel optimization

    Jonathan Eckstein

  • Augmented Lagrangian and Alternating Direction Methods for Convex Optimization: A Tutorial and Some Illustrative Computational Results

    Jonathan Eckstein

  • Approximate iterations in Bregman-function-based proximal algorithms

    Jonathan Eckstein

  • Parallel alternating direction multiplier decomposition of convex programs

    J. Eckstein

  • Some Reformulations and Applications of the Alternating Direction Method of Multipliers

    Jonathan Eckstein;Masao Fukushima

  • Pico: An Object-Oriented Framework for Parallel Branch and Bound *

    Jonathan Eckstein;Cynthia A. Phillips;William E. Hart

  • Some Saddle-function splitting methods for convex programming

    Jonathan Eckstein

  • Parallel Branch-and-Bound Algorithms for General Mixed Integer Programming on the CM-5

    Jonathan Eckstein

  • Dual coordinate step methods for linear network flow problems

    D. P. Bertsekas;J. Eckstein

  • Stochastic dedication: designing fixed income portfolios using massively parallel Benders decomposition

    Randall S. Hiller;Jonathan Eckstein

  • Operator-Splitting Methods for Monotone Affine Variational Inequalities, with a Parallel Application to Optimal Control

    Jonathan Eckstein;Michael C. Ferris

  • Distributed Asynchronous Relaxation Methods for Linear Network Flow Problems

    D.P. Bertsekas;J. Eckstein

  • A family of projective splitting methods for the sum of two maximal monotone operators

    Jonathan Eckstein;B. F. Svaiter

  • General Projective Splitting Methods for Sums of Maximal Monotone Operators

    Jonathan Eckstein;B. F. Svaiter

  • The Maximum Box Problem and its Application to Data Analysis

    Jonathan Eckstein;Peter L. Hammer;Ying Liu;Mikhail Nediak

  • Asynchronous block-iterative primal-dual decomposition methods for monotone inclusions

    Patrick L. Combettes;Jonathan Eckstein

  • A practical relative error criterion for augmented Lagrangians

    Jonathan Eckstein;Paulo Jose da Silva e Silva

Frequent Co-Authors

Cynthia A. Phillips
Cynthia A. Phillips Sandia National Laboratories
William E. Hart
William E. Hart Sandia National Laboratories
Dimitri P. Bertsekas
Dimitri P. Bertsekas Arizona State University
Avigdor Gal
Avigdor Gal Technion – Israel Institute of Technology
Benar Fux Svaiter
Benar Fux Svaiter Instituto Nacional de Matemática Pura e Aplicada
Jean-Paul Watson
Jean-Paul Watson Lawrence Livermore National Laboratory
Michael C. Ferris
Michael C. Ferris University of Wisconsin–Madison
Patrick L. Combettes
Patrick L. Combettes North Carolina State University
Stefano Basagni
Stefano Basagni Northeastern University
Chiara Petrioli
Chiara Petrioli Sapienza University of Rome

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