20 citations · 47 across the 8 of their papers we have counts for
9 papers
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn +1
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems. It can solve min-max and noncooperative game formulation…
Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms
M. Marques Alves, Jonathan Eckstein, Marina Geremia +1
This paper derives new inexact variants of the Douglas-Rachford splitting method for maximal monotone operators and the alternating direction method of multipliers (ADMM) for conve…
Single-Forward-Step Projective Splitting: Exploiting Cocoercivity
Patrick R. Johnstone, Jonathan Eckstein
This work describes a new variant of projective splitting for solving maximal monotone inclusions and complicated convex optimization problems. In the new version, cocoercive opera…
Efficient Distributed-Memory Parallel Matrix-Vector Multiplication with Wide or Tall Unstructured Sparse Matrices
Jonathan Eckstein, Gyorgy Matyasfalvi
This paper presents an efficient technique for matrix-vector and vector-transpose-matrix multiplication in distributed-memory parallel computing environments, where the matrices ar…
Projective Splitting with Forward Steps only Requires Continuity
Patrick R. Johnstone, Jonathan Eckstein
A recent innovation in projective splitting algorithms for monotone operator inclusions has been the development of a procedure using two forward steps instead of the customary pro…
Convergence Rates for Projective Splitting
Patrick R. Johnstone, Jonathan Eckstein
Projective splitting is a family of methods for solving inclusions involving sums of maximal monotone operators. First introduced by Eckstein and Svaiter in 2008, these methods hav…