Please use this identifier to cite or link to this item: https://doi.org/10.1137/S1052623402361447
Title: A decomposition method based on SQP for a class of multistage stochastic nonlinear programs
Authors: Liu, X. 
Zhao, G. 
Keywords: Decomposition
Multistage stochastic nonlinear programs
Scenario analysis
Sequential quadratic programming
Issue Date: 2004
Source: Liu, X.,Zhao, G. (2004). A decomposition method based on SQP for a class of multistage stochastic nonlinear programs. SIAM Journal on Optimization 14 (1) : 200-222. ScholarBank@NUS Repository. https://doi.org/10.1137/S1052623402361447
Abstract: Multistage stochastic programming problems arise in many practical situations, such as production and manpower planning, portfolio selections, and so on. In general, the deterministic equivalents of these problems can be very large and may not be solvable directly by general-purpose optimization approaches. Sequential quadratic programming (SQP) methods are very effective for solving medium-size nonlinear programming. By using the scenario analysis technique, a decomposition method based on SQP for solving a class of multistage stochastic nonlinear programs is proposed, which generates the search direction by solving parallelly a set of quadratic programming subproblems with much less size than the original problem at each iteration. Conjugate gradient methods can be introduced to derive the estimates of the dual multiplier associated with the nonanticipativity constraints. By selecting the step-size to reduce an exact penalty function sufficiently, the algorithm terminates finitely at an approximate optimal solution to the problem with any desirable accuracy. Some preliminary numerical results are reported.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/114292
ISSN: 10526234
DOI: 10.1137/S1052623402361447
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

5
checked on Feb 13, 2018

Page view(s)

38
checked on Feb 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.