Recently,inverse problems have attracted more and more attention in computational mathematics and become increasingly important in engineering applications.After the discretization,many of inverse problems are reduced...Recently,inverse problems have attracted more and more attention in computational mathematics and become increasingly important in engineering applications.After the discretization,many of inverse problems are reduced to linear systems.Due to the typical ill-posedness of inverse problems,the reduced linear systems are often illposed,especially when their scales are large.This brings great computational difficulty.Particularly,a small perturbation in the right side of an ill-posed linear system may cause a dramatical change in the solution.Therefore,regularization methods should be adopted for stable solutions.In this paper,a new class of accelerated iterative regularization methods is applied to solve this kind of large-scale ill-posed linear systems.An iterative scheme becomes a regularization method only when the iteration is early terminated.And a Morozov’s discrepancy principle is applied for the stop criterion.Compared with the conventional Landweber iteration,the new methods have acceleration effect,and can be compared to the well-known acceleratedν-method and Nesterov method.From the numerical results,it is observed that using appropriate discretization schemes,the proposed methods even have better behavior when comparing withν-method and Nesterov method.展开更多
In this paper,domain decomposition method（DDM） for numerical solutions of mathematical physics equations is improved into dynamic domain decomposition method（DDDM） . The main feature of the DDDM is that the number...In this paper,domain decomposition method（DDM） for numerical solutions of mathematical physics equations is improved into dynamic domain decomposition method（DDDM） . The main feature of the DDDM is that the number,shape and volume of the sub-domains are all flexibly changeable during the iterations,so it suits well to be implemented on a reconfigurable parallel computing system. Convergence analysis of the DDDM is given,while an application approach to a weak nonlinear elliptic boundary value problem and a numerical experiment are discussed.展开更多
基金supported by the Natural Science Foundation of China (Nos. 11971230, 12071215)the Fundamental Research Funds for the Central Universities(No. NS2018047)the 2019 Graduate Innovation Base(Laboratory)Open Fund of Jiangsu Province(No. Kfjj20190804)
文摘Recently,inverse problems have attracted more and more attention in computational mathematics and become increasingly important in engineering applications.After the discretization,many of inverse problems are reduced to linear systems.Due to the typical ill-posedness of inverse problems,the reduced linear systems are often illposed,especially when their scales are large.This brings great computational difficulty.Particularly,a small perturbation in the right side of an ill-posed linear system may cause a dramatical change in the solution.Therefore,regularization methods should be adopted for stable solutions.In this paper,a new class of accelerated iterative regularization methods is applied to solve this kind of large-scale ill-posed linear systems.An iterative scheme becomes a regularization method only when the iteration is early terminated.And a Morozov’s discrepancy principle is applied for the stop criterion.Compared with the conventional Landweber iteration,the new methods have acceleration effect,and can be compared to the well-known acceleratedν-method and Nesterov method.From the numerical results,it is observed that using appropriate discretization schemes,the proposed methods even have better behavior when comparing withν-method and Nesterov method.
基金Supported by the Foundation of National Defence Key Laboratory (51484020305JW1206)
文摘In this paper,domain decomposition method（DDM） for numerical solutions of mathematical physics equations is improved into dynamic domain decomposition method（DDDM） . The main feature of the DDDM is that the number,shape and volume of the sub-domains are all flexibly changeable during the iterations,so it suits well to be implemented on a reconfigurable parallel computing system. Convergence analysis of the DDDM is given,while an application approach to a weak nonlinear elliptic boundary value problem and a numerical experiment are discussed.
