In this paper, a new two-step Newton-type method with third-order convergence for solving systems of nonlinear equations is proposed. We construct the new method based on the integral interpolation of Newton’s method...In this paper, a new two-step Newton-type method with third-order convergence for solving systems of nonlinear equations is proposed. We construct the new method based on the integral interpolation of Newton’s method. Its cubic convergence and error equation are proved theoretically, and demonstrated numerically. Its application to systems of nonlinear equations and boundary-value problems of nonlinear ODEs are shown as well in the numerical examples.展开更多
In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set...In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set-valued mapping with closed graph.We establish the convergence criteria of the restricted inexact Newton-type method,which guarantees the existence of any sequence generated by this method and show this generated sequence is convergent linearly and quadratically according to the particular assumptions on the Frechet derivative of f.Indeed,we obtain semilocal and local convergence results of restricted inexact Newton-type method for solving the above generalized equation when the Frechet derivative of f is continuous and Lipschitz continuous as well as f+F is metrically regular.An application of this method to variational inequality is given.In addition,a numerical experiment is given which illustrates the theoretical result.展开更多
Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique.Different from existing thresholding methods,a novel threshold...Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique.Different from existing thresholding methods,a novel thresholding technique referred to as the optimal k-thresholding was recently proposed by Zhao(SIAM J Optim 30(1):31-55,2020).This technique simultaneously performs the minimization of an error metric for the problem and thresholding of the iterates generated by the classic gradient method.In this paper,we propose the so-called Newton-type optimal k-thresholding(NTOT)algorithm which is motivated by the appreciable performance of both Newton-type methods and the optimal k-thresholding technique for signal recovery.The guaranteed performance(including convergence)of the proposed algorithms is shown in terms of suitable choices of the algorithmic parameters and the restricted isometry property(RIP)of the sensing matrix which has been widely used in the analysis of compressive sensing algorithms.The simulation results based on synthetic signals indicate that the proposed algorithms are stable and efficient for signal recovery.展开更多
文摘In this paper, a new two-step Newton-type method with third-order convergence for solving systems of nonlinear equations is proposed. We construct the new method based on the integral interpolation of Newton’s method. Its cubic convergence and error equation are proved theoretically, and demonstrated numerically. Its application to systems of nonlinear equations and boundary-value problems of nonlinear ODEs are shown as well in the numerical examples.
基金supported by CAS-President International Fellowship Initiative(PIFI)from the Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing,China.
文摘In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set-valued mapping with closed graph.We establish the convergence criteria of the restricted inexact Newton-type method,which guarantees the existence of any sequence generated by this method and show this generated sequence is convergent linearly and quadratically according to the particular assumptions on the Frechet derivative of f.Indeed,we obtain semilocal and local convergence results of restricted inexact Newton-type method for solving the above generalized equation when the Frechet derivative of f is continuous and Lipschitz continuous as well as f+F is metrically regular.An application of this method to variational inequality is given.In addition,a numerical experiment is given which illustrates the theoretical result.
基金founded by the National Natural Science Foundation of China(No.12071307).
文摘Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique.Different from existing thresholding methods,a novel thresholding technique referred to as the optimal k-thresholding was recently proposed by Zhao(SIAM J Optim 30(1):31-55,2020).This technique simultaneously performs the minimization of an error metric for the problem and thresholding of the iterates generated by the classic gradient method.In this paper,we propose the so-called Newton-type optimal k-thresholding(NTOT)algorithm which is motivated by the appreciable performance of both Newton-type methods and the optimal k-thresholding technique for signal recovery.The guaranteed performance(including convergence)of the proposed algorithms is shown in terms of suitable choices of the algorithmic parameters and the restricted isometry property(RIP)of the sensing matrix which has been widely used in the analysis of compressive sensing algorithms.The simulation results based on synthetic signals indicate that the proposed algorithms are stable and efficient for signal recovery.
基金Supported by the National Natural Science Foundation of China(10231060,10501024)the Specialized Research Fund of Doc-toral Program of Higher Education of China(20040319003)+1 种基金the Natural Science Fund of Jiangsu Province(BK2006214)the Key Subject Fund of Nanjing Normal University
