Risk-Neutral Stochastic Linear Programming Methods

Lewis Ntaimo
Additional contact information
Lewis Ntaimo: Texas A&M University

Chapter Chapter 6 in Computational Stochastic Programming, 2024, pp 223-273 from Springer

Abstract: Abstract In this chapter, we study decomposition methods for risk-neutral two-stage stochastic linear programming (RN-SLP). We use the theoretical properties of the models derived in Chap. 2 and decomposition techniques from Chap. 5 to derive solution algorithms for RN-SLP. We begin our study with the classical L-shaped algorithm in Sect. 6.2, which generates a single optimality cut at a given iteration of the algorithm to approximate the recourse function. We then consider two extensions of the algorithm; namely, the multicut algorithm in Sect. 6.3 and the adaptive multicut algorithm in Sect. 6.4. Instead of generating a single optimality cut at a given iteration of the algorithm, the multicut method generates multiple optimality cuts, one for each scenario. In the adaptive multicut method an arbitrary aggregation of the cuts is permitted to potentially speed up computation time. computer implementation (coding) of decomposition algorithms for RN-SLP may not be an easy activity for many students. Therefore, in our derivation of the various algorithms, we place emphasis on implementation and provide guidelines for efficient computer codes. We end the chapter with a list of other decomposition methods for RN-SLP not covered in this chapter in Sect. 6.5.

Date: 2024
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:spochp:978-3-031-52464-6_6

Ordering information: This item can be ordered from
http://www.springer.com/9783031524646

DOI: 10.1007/978-3-031-52464-6_6

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().