scholarly journals Group classification of semilinear Kohn–Laplace equations

2008 ◽  
Vol 68 (9) ◽  
pp. 2552-2568 ◽  
Author(s):  
Yuri Bozhkov ◽  
Igor Leite Freire
Keyword(s):  
Group Classification ◽  
Laplace Equations

Lie group classification of the N-th-order nonlinear evolution equations

2011 ◽  
Vol 54 (12) ◽  
pp. 2553-2572 ◽  
Author(s):  
ShouFeng Shen ◽  
ChangZheng Qu ◽  
Qing Huang ◽  
YongYang Jin
Keyword(s):  
Lie Group ◽  
Evolution Equations ◽  
Nonlinear Evolution ◽  
Group Classification ◽  
Nonlinear Evolution Equations ◽  
Lie Group Classification

scholarly journals Group Classification of a General Bond-Option Pricing Equation of Mathematical Finance

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tanki Motsepa ◽  
Chaudry Masood Khalique ◽  
Motlatsi Molati
Keyword(s):  
Option Pricing ◽  
Three Dimensional ◽  
Mathematical Finance ◽  
Group Classification ◽  
Lie Point Symmetries ◽  
Group Invariant ◽  
Black Scholes ◽  
Group Invariant Solutions ◽  
Bond Option

We carry out group classification of a general bond-option pricing equation. We show that the equation admits a three-dimensional equivalence Lie algebra. We also show that some of the values of the constants which result from group classification give us well-known models in mathematics of finance such as Black-Scholes, Vasicek, and Cox-Ingersoll-Ross. For all such values of these arbitrary constants we obtain Lie point symmetries. Symmetry reductions are then obtained and group invariant solutions are constructed for some cases.

Lie group classification of second-order ordinary difference equations

2000 ◽  
Vol 41 (1) ◽  
pp. 480-504 ◽  
Author(s):  
Vladimir Dorodnitsyn ◽  
Roman Kozlov ◽  
Pavel Winternitz
Keyword(s):  
Lie Group ◽  
Difference Equations ◽  
Group Classification ◽  
Second Order ◽  
Lie Group Classification

scholarly journals Abductive Networks For Two-Group Classification: A Comparison With Neural Networks

2011 ◽  
Vol 15 (2) ◽  
pp. 1 ◽  
Author(s):  
Anurag Agarwal
Keyword(s):  
Neural Networks ◽  
Linear Mapping ◽  
Group Classification ◽  
Type Ii ◽  
Artificial Intelligence Technique ◽  
Empirical Tests ◽  
Multivariate Discriminant ◽  
Better Than

<span>In this study, a new Artificial Intelligence technique for non-linear mapping called Abductive Networks is used for two-group classification of firms. The results are compared with Neural Networks, another AI technique, which has been shown to perform better than the traditional statistical techniques such as multivariate discriminant analysis and logit. In empirical tests, Abductive Networks perform as well or better than Neural Networks on various criteria of measurement such as Type 1 / Type II accuracy criteria and Distance Between Centroids.</span>

scholarly journals Algebraic Method for Group Classification of (1+1)-Dimensional Linear Schrödinger Equations

2018 ◽  
Vol 157 (1) ◽  
pp. 171-203 ◽  
Author(s):  
Célestin Kurujyibwami ◽  
Peter Basarab-Horwath ◽  
Roman O. Popovych
Keyword(s):  
Algebraic Method ◽  
Group Classification ◽  
Schrodinger Equations ◽  
Schrödinger Equations

scholarly journals Enhanced group classification of Benjamin–Bona–Mahony–Burgers equations

2017 ◽  
Vol 65 ◽  
pp. 19-25 ◽  
Author(s):  
Olena Vaneeva ◽  
Severin Pošta ◽  
Christodoulos Sophocleous
Keyword(s):  
Group Classification ◽  
Burgers Equations
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