Abstract
In Chapter 4, we showed that when a graph is embedded in a suitably constructed Markov decision process, the associated convex domain of discounted occupational measures is a polyhedron with extreme points corresponding to all spanning subgraphs of the given graph. Furthermore, from Theorem 4.1 we learned that a simple cut of the above domain yields a polyhedron the extreme points of which correspond to only two possible types: Hamiltonian cycles and convex combinations of short and noose cycles. These properties, naturally, suggest certain algorithmic approaches to searching for Hamiltonian cycles.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Borkar, V.S., Ejov, V., Filar, J.A., Nguyen, G.T. (2012). Linear Programming Based Algorithms. In: Hamiltonian Cycle Problem and Markov Chains. International Series in Operations Research & Management Science, vol 171. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3232-6_7
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3232-6_7
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3231-9
Online ISBN: 978-1-4614-3232-6
eBook Packages: Business and EconomicsBusiness and Management (R0)