Interior Point and Cross-Entropy Algorithms

Vivek S. Borkar (), Vladimir Ejov (), Jerzy A. Filar () and Giang T. Nguyen ()
Additional contact information
Vivek S. Borkar: IIT, Powai
Vladimir Ejov: Flinders University
Jerzy A. Filar: Flinders University
Giang T. Nguyen: Université libre de Bruxelles

Chapter Chapter 8 in Hamiltonian Cycle Problem and Markov Chains, 2012, pp 143-159 from Springer

Abstract: Abstract In this chapter, we brie y discuss two recent algorithms that exploit two modern trends in optimisation in the context of our stochastic embedding of the Hamiltonian cycle problem: the interior point method and the importance sampling method. In particular, the first algorithm searches in the interior of the convex domain of doubly stochastic matrices induced by a given graph, with the goal of converging to an extreme point corresponding to a permutation matrix that coincides with a Hamiltonian cycle.

Keywords: Interior Point; Markov Decision Process; Hamiltonian Cycle; Interior Point Method; Probability Transition Matrix (search for similar items in EconPapers)
Date: 2012
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:978-1-4614-3232-6_8

Ordering information: This item can be ordered from
http://www.springer.com/9781461432326

DOI: 10.1007/978-1-4614-3232-6_8

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 ().