Template-Type: ReDIF-Paper 1.0
Author-Name: Berkelaar, A.B.
Author-Name-Last: Berkelaar
Author-Name-First: Arjan
Author-Name: Dert, C.L.
Author-Name-Last: Dert
Author-Name-First: Cees
Author-Name: Oldenkamp, K.P.B.
Author-Name-Last: Oldenkamp
Author-Name: Zhang, S.
Author-Name-Last: Zhang
Author-Name-First: Shuzhong
Title: A primal-dual decomposition based interior point approach to two-stage stochastic linear programming
Abstract: Decision making under uncertainty is a challenge faced by many decision makers. Stochastic programming is a major tool developed to deal with optimization with uncertainties  that has found applications in, e.g. finance, such as asset-liability and bond-portfolio management. Computationally however, many models in stochastic programming remain unsolvable because of overwhelming dimensionality. For a model to be well solvable, its special structure must be explored. Most of the solution methods are based on decomposing the data. In this paper we propose a new decomposition approach for two-stage stochastic programming, based on a direct application of the path-following method combined with the homogeneous self-dual technique. Numerical experiments show that our decomposition algorithm is very efficient for solving stochastic programs. In particular, we apply our deompostition method to a two-period portfolio selection problem using options on a stock index. In this model the investor can invest in a money-market account, a stock index, and European
 options on this index with different maturities. We experiment our model with market prices of options on the S&P500.
Creation-Date: 1999-04-26
File-URL: https://repub.eur.nl/pub/1588/1588_ps.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 9918-/A
Classification-JEL: C61, G11
Keywords: decomposition methods, large scale problems, optimization techniques, portfolio choice, stochastic programming
Handle: RePEc:ems:eureir:1588