A computationally efficient FPTAS for convex stochastic dynamic programs

Nir Halman, Giacomo Nannicini, James Orlin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose a computationally efficient Fully Polynomial-Time Approximation Scheme (FPTAS) for convex stochastic dynamic programs using the technique of K-approximation sets and functions introduced by Halman et al. This paper deals with the convex case only, and it has the following contributions: First, we improve on the worst-case running time given by Halman et al. Second, we design an FPTAS with excellent computational performance, and show that it is faster than an exact algorithm even for small problem instances and small approximation factors, becoming orders of magnitude faster as the problem size increases. Third, we show that with careful algorithm design, the errors introduced by floating point computations can be bounded, so that we can provide a guarantee on the approximation factor over an exact infinite-precision solution. Our computational evaluation is based on randomly generated problem instances coming from applications in supply chain management and finance.

Original languageEnglish
Title of host publicationAlgorithms, ESA 2013 - 21st Annual European Symposium, Proceedings
Pages577-588
Number of pages12
DOIs
StatePublished - 2013
Externally publishedYes
Event21st Annual European Symposium on Algorithms, ESA 2013 - Sophia Antipolis, France
Duration: 2 Sep 20134 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8125 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Annual European Symposium on Algorithms, ESA 2013
Country/TerritoryFrance
CitySophia Antipolis
Period2/09/134/09/13

Fingerprint

Dive into the research topics of 'A computationally efficient FPTAS for convex stochastic dynamic programs'. Together they form a unique fingerprint.

Cite this