2022
DOI: 10.3390/math10152798
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Updating the Landweber Iteration Method for Solving Inverse Problems

Abstract: The Landweber iteration method is one of the most popular methods for the solution of linear discrete ill-posed problems. The diversity of physical problems and the diversity of operators that result from them leads us to think about updating the main methods and algorithms to achieve the best results. We considered in this work the linear operator equation and the use of a new version of the Landweber iterative method as an iterative solver. The main goal of updating the Landweber iteration method is to make … Show more

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Cited by 10 publications
(4 citation statements)
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“…(1) or to an equation of the error. It is called residual equation [28] . Let be an approximation of , then the error satisfies the equation where r is the residual, e.g.…”
Section: Problem Statementmentioning
confidence: 99%
“…(1) or to an equation of the error. It is called residual equation [28] . Let be an approximation of , then the error satisfies the equation where r is the residual, e.g.…”
Section: Problem Statementmentioning
confidence: 99%
“…In an environment with an increasing energy shortage, source term identification helps to find new energy. A variety of regularization methods are proposed by scholars to deal with inverse problems, which include, for example, the Tikhonov regularization method [8,9], Fourier regularization method [10,11], Quasi-boundary value regularization method [12,13], Quasi-reversibility regularization method [14,15], truncation regularization method [16,17], Landweber iterative regularization method [18,19], etc. This paper will use the Tikhonov regularization method and Quasi-boundary regularization method to deal with the problem of source term identification.…”
Section: Introductionmentioning
confidence: 99%
“…The ill-posed property makes the parameter field susceptible to the noise in the measurement data, while the non-linear dependence of the measurement data with respect to the parameter field causes the presence of numerous local minima. For the non-linear ill-posed problem, conventional linearized methods, such as the Gauss-Newton method [7], Landweber method [8], Levenberg-Marquardt method [9], are locally convergent. The recent popular methods (e.g., trust region algorithm [10], neural networks algorithm [11], genetic algorithm [12], simulated annealing algorithm [13]) have global convergence properties, but the efficiency is much worse than before, along with the searching space decreasing.…”
Section: Introductionmentioning
confidence: 99%