Error Bounds for the SSOR Semi-Iterative Method

1985 ◽  
Vol 6 (2) ◽  
pp. 245-250 ◽  
Author(s):  
Lala B. Krishna
Keyword(s):  
Iterative Method ◽  
Error Bounds

scholarly journals Semi-Iterative Method for Computing the Generalized InverseAT,S(2)

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xiaoji Liu ◽  
Caijing Jiang
Keyword(s):  
Iterative Method ◽  
Banach Spaces ◽  
Error Bounds ◽  
Generalized Inverse

The main aim of this paper is to compute the generalized inverseAT,S(2)over Banach spaces by using semi-iterative method and to present the error bounds of the semi-iterative method for approximatingAT,S(2).

scholarly journals A new iterative method for computing the Drazin inverse

Filomat ◽  
2012 ◽  
Vol 26 (3) ◽  
pp. 597-606 ◽  
Author(s):  
Jin Zhong ◽  
Xiaoji Liu ◽  
Guangping Zhou ◽  
Yaoming Yu
Keyword(s):  
Iterative Method ◽  
Iterative Methods ◽  
Error Bounds ◽  
Drazin Inverse ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Necessary And Sufficient ◽  
New Iterative Method

In this paper, we construct a new iterative method for computing the Drazin inverse and deduce the necessary and sufficient condition for its convergence to Ad. Moreover, we present the error bounds of the iterative methods for approximating Ad.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

An adaptive chebyshev iterative method

2018 ◽  
Vol 30 (10) ◽  
pp. 67-85
Author(s):  
V. Zhukov ◽  
◽  
O. Feodoritova ◽  
N. Novikova ◽  
◽  
...  
Keyword(s):  
Iterative Method
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