Expected Computation Time for Hamiltonian Path problem

1987 ◽  
Vol 16 (3) ◽  
pp. 486-502 ◽  
Author(s):  
Yuri Gurevich ◽  
Saharon Shelah
1985 ◽  
Vol 10 (2) ◽  
pp. 179-195 ◽  
Author(s):  
Gerald L. Thompson ◽  
Sharad Singhal

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
M. Sohel Rahman ◽  
M. Kaykobad ◽  
Jesun Sahariar Firoz

A Hamiltonian path in a graph is a path involving all the vertices of the graph. In this paper, we revisit the famous Hamiltonian path problem and present new sufficient conditions for the existence of a Hamiltonian path in a graph.


2021 ◽  
Vol 16 (5) ◽  
pp. 731-737
Author(s):  
Jingjing Ma

Self-assembly reveals the innate character of DNA computing, DNA self-assembly is regarded as the best way to make DNA computing transform into computer chip. This paper introduces a strategy of DNA 3D self-assembly algorithm to solve the Hamiltonian Path Problem. Firstly, I introduced a non-deterministic algorithm. Then, according to the algorithm I designed the types of DNA tiles which the computing process needs. Lastly, I demonstrated the self-assembly process and the experimental methods which can get the final result. The computing time is linear, and the number of the different tile types is constant.


2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Fatemeh Keshavarz-Kohjerdi ◽  
Alireza Bagheri

The Hamiltonian path problem for general grid graphs is known to be NP-complete. In this paper, we give necessary and sufficient conditions for the existence of Hamiltonian paths inL-alphabet,C-alphabet,F-alphabet, andE-alphabet grid graphs. We also present linear-time algorithms for finding Hamiltonian paths in these graphs.


1999 ◽  
Vol 09 (04) ◽  
pp. 539-550 ◽  
Author(s):  
JEAN CARLE ◽  
JEAN-FREDERIC MYOUPO ◽  
DAVID SEME

This paper presents two simple all-to-all broadcasting algorithms on honeycomb mesh. Consider a network with n processors, one has personalized routing strategy at each node and it requires a 3n communication time complexity. This communication time can be reduced to n because the computation time is always assumed to be much lower than the communication time. The other is based on a Hamiltonian path and has a 2n communication time complexity. We show how they can be used to get parallel solutions to a class of problems on honeycomb networks, among others Prefix Sums, Maximal Vectors, Maximal Sum Subsegment, Parenthesis Matching, Decoding Binary Tree, and Sorting. In our knowledge, these all-to-all broadcast algorithms are the only ones so far exhibited on a honeycomb.


1994 ◽  
Vol 50 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Axel Conrad ◽  
Tanja Hindrichs ◽  
Hussein Morsy ◽  
Ingo Wegener

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