Efficient algorithms for the recognition of topologically conjugate gradient-like diffeomorhisms


Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

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.

Sobre autores

Vyacheslav Grines

National Research University Higher School of Economics

Autor responsável pela correspondência
Email: vgrines@yandex.ru
Rússia, ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155

Dmitry Malyshev

National Research University Higher School of Economics; N. I. Lobachevsky State University of Nizhni Novgorod

Email: vgrines@yandex.ru
Rússia, ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155; ul. Gagarina 23, Nizhny Novgorod, 603950

Olga Pochinka

National Research University Higher School of Economics

Email: vgrines@yandex.ru
Rússia, ul. Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155

Svetlana Zinina

Ogarev Mordovia State University

Email: vgrines@yandex.ru
Rússia, ul. Bolshevistskaya 68, Saransk, 430005

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML

Declaração de direitos autorais © Pleiades Publishing, Ltd., 2016