scholarly journals Ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints

Author(s):  
Leyang Wang ◽  
Tao Chen
2013 ◽  
Vol 416-417 ◽  
pp. 1289-1295
Author(s):  
Chao Zhong Ma ◽  
Ji Fu ◽  
Yuan Lu Du ◽  
Qing Ming Gui ◽  
Yong Wei Gu

Based on non-precision observation, it researches the inversion problem with morbid equality constraints. And according to the pathological problems exist for the coefficient matrix and the constraint matrix, and it suggests the ridge estimation of the double-k type derived Ridge to determine these parameters. The results show that a variety of programs and double k ridge estimate not only removes the constraint matrix morbid adverse effects, but also can better overcome the master model morbidity and constraint matrix caused by the presence of instability, which is a good estimate.


2020 ◽  
Author(s):  
tieding lu

<p> Uncertainties usually exist in the process of acquisition of measurement data, which affect the results of the parameter estimation. The solution of the uncertainty adjustment model can effectively improve the validity and reliability of parameter estimation. When the coefficient matrix of the observation equation has a singular value close to zero, i.e., the coefficient matrix is ill-posed, the ridge estimation can effectively suppress the influence of the ill-posed problem of the observation equation on the parameter estimation. When the uncertainty adjustment model is ill-posed, it is more seriously affected by the error of the coefficient matrix and observation vector. In this paper, the ridge estimation method is applied to ill-posed uncertainty adjustment model, deriving an iterative algorithm to improve the stability and reliability of the results. The derived algorithm is verified by two examples, and the results show that the new method is effective and feasible.</p>


1995 ◽  
Author(s):  
Daniela Calvetti ◽  
Lothar Reichel ◽  
Q. Zhang

2020 ◽  
Vol 36 (3) ◽  
pp. 475-482
Author(s):  
HONG-KUN XU ◽  
NAJLA ALTWAIJRY ◽  
SOUHAIL CHEBBI

We consider an iterative method for regularization of a variational inequality (VI) defined by a Lipschitz continuous monotone operator in the case where the set of feasible solutions is decomposed to the intersection of finitely many closed convex subsets of a Hilbert space. We prove the strong convergence of the sequence generated by our algorithm. It seems that this is the first time in the literature to handle iterative solution of ill-posed VIs in the domain decomposition case.


2016 ◽  
Author(s):  
Qinpeng Liu ◽  
Haiwei Fu ◽  
Zhen’an Jia ◽  
Dakuan Yu ◽  
Gao Hong

2010 ◽  
Vol 15 (4) ◽  
pp. 265-274 ◽  
Author(s):  
A. AghaKouchak ◽  
E. Habib ◽  
A. Bárdossy

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