A Probabilistic Lower Bound for Two-Stage Stochastic Programs

George B. Dantzig () and Gerd Infanger ()
Additional contact information
George B. Dantzig: Stanford University
Gerd Infanger: Stanford University

Chapter Chapter 2 in Stochastic Programming, 2010, pp 13-35 from Springer

Abstract: Abstract In the framework of Benders decomposition for two-stage stochastic linear programs, we estimate the coefficients and right-hand sides of the cutting planes using Monte Carlo sampling. We present a new theory for estimating a lower bound for the optimal objective value and we compare (using various test problems whose true optimal value is known) the predicted versus the observed rate of coverage of the optimal objective by the lower bound confidence interval.

Keywords: Master Problem; Bender Decomposition; Normal Deviate; Variance Reduction Technique; Bender Decomposition Algorithm (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-1642-6_2

Ordering information: This item can be ordered from
http://www.springer.com/9781441916426

DOI: 10.1007/978-1-4419-1642-6_2

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().