Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation

Jonathan Eckstein (), Jean-Paul Watson () and David L. Woodruff ()
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Jonathan Eckstein: Department of Management Science and Information Systems, Rutgers Business School Newark and New Brunswick, Rutgers University, Piscataway, New Jersey 08854
Jean-Paul Watson: Center for Applied Scientific Computing and Global Security Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550
David L. Woodruff: Graduate School of Management, University of California, Davis, California 95616

Operations Research, 2025, vol. 73, issue 1, 311-324

Abstract: We propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We derive the method from a class of projective operator splitting methods fairly recently proposed by Combettes and Eckstein, significantly expanding the known applications of those methods. Our derivation assures convergence for convex problems whose feasible set is compact, subject to some standard regularity conditions and a mild “fairness” condition on subproblem selection. The method’s convergence guarantees are deterministic and do not require randomization, in contrast to other proposed asynchronous variations of progressive hedging. Computational experiments described in an online appendix show the method to outperform progressive hedging on large-scale problems in a highly parallel computing environment.

Keywords: Optimization; stochastic programming; high performance computing; decomposition (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:73:y:2025:i:1:p:311-324

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