Titlebook: Hamiltonian Cycle Problem and Markov Chains; Vivek S. Borkar,Vladimir Ejov,Giang T. Nguyen Book 2012 Springer Science+Business Media, LLC

Maximizer

Psychogenic

Markov Decision Processesparticular, in the context of this book, we observe that in any given graph Hamiltonian cycles (if any) correspond to a family of spanning subgraphs inducing very special Markov chains whose probability transition matrices are a subset of permutation matrices possessing only a single ergodic class.

体贴

倒转

Choreography

Linear Programming Based Algorithmsearned 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.

homeostasis

Self-similar Structure and Hamiltonicityexample, Meringer [77]). This offers an opportunity to study the whole populations of these graphs with the goal of understanding the special nature of those members of that population that correspond to non-Hamiltonian graphs.

syring

ULCER

Graph Enumerationa given connectivity. In comparison to labeled cubic graphs, the numeration of unlabeled cubic graphs is a significantly more challenging problem [102]. In 1977, Robinson [88] presented a method to count unlabeled cubic graphs.

一大群

Rinne-Test

https://doi.org/10.1007/978-1-349-27476-5 recent and comprehensive treatment on probabilistic methods). Similarly, connections between Markov chains and graph theory have long been made (see Harary [57]). Our contribution here is to apply properties of Markov chains to the Hamiltonian cycle problem and to take advantage of the still emergi