Decomposition via Alternating Linearization
AbstractA new approximate proximal point method for minimizing the sum of two convex functions is introduced. It replaces the original problem by a sequence of regularized subproblems in which the functions are alternately represented by linear models. The method updates the linear models and the prox center, as well as the prox coefficient. It is monotone in terms of the objective values and converges to a solution of the problem, if any. A dual version of the method is derived and analyzed. Applications of the methods to multistage stochastic programming problems are discussed and preliminary numerical experience presented.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp95051.
Date of creation: Jun 1995
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- A. Ruszczynski, 1994.
"On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs,"
wp94005, International Institute for Applied Systems Analysis.
- C.H. Rosa & A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs," Working Papers wp94125, International Institute for Applied Systems Analysis.
- A. Ruszczynski, 1992. "Augmented Lagrangian Decomposition for Sparse Convex Optimization," Working Papers wp92075, International Institute for Applied Systems Analysis.
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