eprintid: 4085
rev_number: 19
eprint_status: archive
userid: 351
dir: disk0/00/00/40/85
datestamp: 2016-01-15 02:04:02
lastmod: 2021-08-27 17:14:44
status_changed: 2016-01-15 02:04:02
type: monograph
metadata_visibility: show
item_issues_count: 2
creators_name: Gondzio, J.
title: Preconditioned Conjugate Gradients in an Interior Point Method for Two-stage Stochastic Programming
ispublished: pub
internal_subjects: iis_frc
internal_subjects: iis_met
divisions: prog_opt
abstract: We develop a variant of an interior point method for solving two-stage stochastic linear programming problems. The problems are solved in a deterministic equivalent form in which the first stage variables appear as dense columns. To avoid their degrading influence on the adjacency structure AA^T (and the Cholesky factor) an iterative method is applied to compute orthogonal projections. Conjugate gradient algorithm with a structure-exploiting preconditioner is used. 

The method has been applied to solve real-life stochastic optimization problems. Preliminary computational results show the feasibility of the approach for problems with up to 80 independent scenarios (a deterministic equivalent linear program has 14001 constraints and 63690 variables).
date: 1994-12
date_type: published
publisher: WP-94-130
iiasapubid: WP-94-130
price: 10
full_text_status: public
monograph_type: working_paper
place_of_pub: IIASA, Laxenburg, Austria
pages: 18
coversheets_dirty: FALSE
fp7_type: info:eu-repo/semantics/book
citation:   Gondzio, J.  (1994).  Preconditioned Conjugate Gradients in an Interior Point Method for Two-stage Stochastic Programming.   IIASA Working Paper. IIASA, Laxenburg, Austria: WP-94-130     
document_url: https://pure.iiasa.ac.at/id/eprint/4085/1/WP-94-130.pdf