Optimization Online


Applying oracles of on-demand accuracy in two-stage stochastic programming - a computational study

Christian Wolf(christian.wolf***at***dsor.de)
Csaba Fábián(fabian.csaba***at***gamf.kefo.hu)
Achim Koberstein(koberstein***at***wiwi.uni-frankfurt.de)
Leena Suhl(suhl***at***dsor.de)

Abstract: Traditionally, two variants of the L-shaped method based on Benders' decomposition principle are used to solve two-stage stochastic programming problems: the single-cut and the multi-cut version. The concept of an oracle with on-demand accuracy was originally proposed in the context of bundle methods for unconstrained convex optimzation to provide approximate function data and subgradients. In this paper, we show how a special form of this concept can be used to devise a variant of the L-shaped method that integrates the advantages of the traditional variants while avoiding their disadvantages. On a set of 104 test problems, we compare and analyze parallel implementations of regularized and unregularized versions of the algorithms. The results indicate that significant speed-ups in computation time can be achieved by applying the concept of on-demand accuracy.

Keywords: Stochastic programming, two-stage problems, decomposition, bundle methods

Category 1: Stochastic Programming

Category 2: Optimization Software and Modeling Systems (Optimization Software Benchmark )


Download: [PDF]

Entry Submitted: 02/17/2014
Entry Accepted: 02/17/2014
Entry Last Modified: 02/17/2014

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society