Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence

2006 ◽  
pp. 214-227 ◽  
Author(s):  
John Oliensis ◽  
Richard Hartley
Keyword(s):  
Algorithm Convergence

A Kind of Preconditioned IGMRES(m) Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 413-416
Author(s):  
Chun Xiao Yu
Keyword(s):  
Krylov Subspace ◽  
Matrix Decomposition ◽  
Coefficient Matrix ◽  
Algebraic Equations ◽  
Residual Method ◽  
Minimal Residual Method ◽  
Algorithm Convergence ◽  
Minimal Residual

Fundamental theories are studied for an Incomplete Generalized Minimal Residual Method(IGMRES(m)) in Krylov subspace. An algebraic equations generated from the IGMRES(m) algorithm is presented. The relationships are deeply researched for the algorithm convergence and the coefficient matrix of the equations. A kind of preconditioned method is proposed to improve the convergence of the IGMRES(m) algorithm. It is proved that the best convergence can be obtained through appropriate matrix decomposition.

Faster learning algorithm convergence utilizing a combined time-frequency representation as basis

2013 ◽  
Author(s):  
A. J. Hendriks ◽  
Hermann Uys ◽  
Anton du Plessis ◽  
Christine Steenkamp
Keyword(s):  
Learning Algorithm ◽  
Time Frequency ◽  
Algorithm Convergence ◽  
Frequency Representation

scholarly journals Nonlinear Analysis: Algorithm, Convergence, and Applications 2014

2014 ◽  
Vol 2014 ◽  
pp. 1-2 ◽  
Author(s):  
Yisheng Song ◽  
Rudong Chen ◽  
Guoyin Li ◽  
Changsen Yang ◽  
Gaohang Yu
Keyword(s):  
Nonlinear Analysis ◽  
Analysis Algorithm ◽  
Algorithm Convergence

scholarly journals Advance in Nonlinear Analysis: Algorithm, Convergence, and Applications

2013 ◽  
Vol 2013 ◽  
pp. 1-2
Author(s):  
Yisheng Song ◽  
Rudong Chen ◽  
Guoyin Li ◽  
Changsen Yang
Keyword(s):  
Nonlinear Analysis ◽  
Analysis Algorithm ◽  
Algorithm Convergence

3-D OBJECT CLASSIFICATION: APPLICATION OF A CONSTRUCTIVE ALGORITHM

1991 ◽  
Vol 02 (04) ◽  
pp. 275-282 ◽  
Author(s):  
Neil Burgess ◽  
Mario Notturno Granieri ◽  
Stefano Patarnello
Keyword(s):  
Object Classification ◽  
Arbitrary Orientation ◽  
Generalization Capability ◽  
Constructive Algorithm ◽  
Algorithm Convergence ◽  
Edge Points ◽  
Training Examples

A system for the classification of real 3-D objects is presented. Ten objects are presented in arbitrary orientation (and position, within limits). The perception of an object is achieved by the use of multiple stereo pairs of images taken from different view positions. Classification of the spectrum of distances between edge-points perceived on an object is achieved using a constructive algorithm. Convergence to zero errors on the set of training examples is guaranteed. The generalization capability is tested on a set of 10–15 novel presentations of each object.

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