%0 Report
%9 IIASA Working Paper
%A Rosa, C.H.
%A Ruszczynski, A.
%C IIASA, Laxenburg, Austria
%D 1994
%F iiasa:4089
%T On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs
%U https://pure.iiasa.ac.at/id/eprint/4089/
%X A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios and by decomposing it into nodes corresponding to stages. Theoretical convergence properties of the two approaches are derived and a computational illustration is presented.