Efficient algorithms for the recognition of topologically conjugate gradient-like diffeomorhisms
- Authors: Grines V.Z.1, Malyshev D.S.1,2, Pochinka O.V.1, Zinina S.K.3
- 
							Affiliations: 
							- National Research University Higher School of Economics
- N. I. Lobachevsky State University of Nizhni Novgorod
- Ogarev Mordovia State University
 
- Issue: Vol 21, No 2 (2016)
- Pages: 189-203
- Section: Article
- URL: https://journals.rcsi.science/1560-3547/article/view/218251
- DOI: https://doi.org/10.1134/S1560354716020040
- ID: 218251
Cite item
Abstract
It is well known that the topological classification of structurally stable flows on surfaces as well as the topological classification of some multidimensional gradient-like systems can be reduced to a combinatorial problem of distinguishing graphs up to isomorphism. The isomorphism problem of general graphs obviously can be solved by a standard enumeration algorithm. However, an efficient algorithm (i. e., polynomial in the number of vertices) has not yet been developed for it, and the problem has not been proved to be intractable (i. e., NPcomplete). We give polynomial-time algorithms for recognition of the corresponding graphs for two gradient-like systems. Moreover, we present efficient algorithms for determining the orientability and the genus of the ambient surface. This result, in particular, sheds light on the classification of configurations that arise from simple, point-source potential-field models in efforts to determine the nature of the quiet-Sun magnetic field.
About the authors
Vyacheslav Z. Grines
National Research University Higher School of Economics
							Author for correspondence.
							Email: vgrines@yandex.ru
				                					                																			                												                	Russian Federation, 							ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155						
Dmitry S. Malyshev
National Research University Higher School of Economics; N. I. Lobachevsky State University of Nizhni Novgorod
														Email: vgrines@yandex.ru
				                					                																			                												                	Russian Federation, 							ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155; ul. Gagarina 23, Nizhny Novgorod, 603950						
Olga V. Pochinka
National Research University Higher School of Economics
														Email: vgrines@yandex.ru
				                					                																			                												                	Russian Federation, 							ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155						
Svetlana Kh. Zinina
Ogarev Mordovia State University
														Email: vgrines@yandex.ru
				                					                																			                												                	Russian Federation, 							ul. Bolshevistskaya 68, Saransk, 430005						
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