In this paper, we are going to present a class of nonlinear equation solving methods. Steffensen’s method is a simple method for solving a nonlinear equation. By using Steffensen’s method and by combining this metho...In this paper, we are going to present a class of nonlinear equation solving methods. Steffensen’s method is a simple method for solving a nonlinear equation. By using Steffensen’s method and by combining this method with it, we obtain a new method. It can be said that this method, due to not using the function derivative, would be a good method for solving the nonlinear equation compared to Newton’s method. Finally, we will see that Newton’s method and Steffensen’s hybrid method both have a two-order convergence.展开更多
In this paper, a one-step Steffensen-type method with super-cubic convergence for solving nonlinear equations is suggested. The convergence order 3.383 is proved theoretically and demonstrated numerically. This super-...In this paper, a one-step Steffensen-type method with super-cubic convergence for solving nonlinear equations is suggested. The convergence order 3.383 is proved theoretically and demonstrated numerically. This super-cubic convergence is obtained by self-accelerating second-order Steffensen’s method twice with memory, but without any new function evaluations. The proposed method is very efficient and convenient, since it is still a derivative-free two-point method. Its theoretical results and high computational efficiency is confirmed by Numerical examples.展开更多
In this paper, seven self-accelerating iterative methods with memory are derived from an optimal two-step Steffensen-type method without memory for solving nonlinear equations, their orders of convergence are proved t...In this paper, seven self-accelerating iterative methods with memory are derived from an optimal two-step Steffensen-type method without memory for solving nonlinear equations, their orders of convergence are proved to be increased,?numerical examples are demonstrat-ed demonstrated to verify the theoretical results, and applications for solving systems of nonlinear equations and BVPs of nonlinear ODEs are illustrated.展开更多
研究了用Newton-Steffensen法求解非线性算子方程.当非线性算子F的一阶导数满足L-平均Lipschitz条件时,建立了Newton-Steffensen法的三阶收敛判据,同时也给出了收敛球半径的估计.作为应用,当F的一阶导数满足经典的Lipschitz条件时或F满...研究了用Newton-Steffensen法求解非线性算子方程.当非线性算子F的一阶导数满足L-平均Lipschitz条件时,建立了Newton-Steffensen法的三阶收敛判据,同时也给出了收敛球半径的估计.作为应用,当F的一阶导数满足经典的Lipschitz条件时或F满足γ-条件时,建立了Newton-Steffensen法的三阶收敛判据及给出了收敛球半径的估计.从而推广了[Journal of Nonlinear and Convex Analysis,2018,19:433-460]中的相应结果.展开更多
提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距...提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距离的线性组合,然后执行HDDI-FCM,在对数据集进行有效聚类的同时进行距离学习.为了保证迭代算法收敛,引入了Steffensen迭代法来改进计算簇中心点的迭代公式.讨论了算法中参数的选择.基于UCI(University of California,Irvine)数据集的实验结果表明该算法是有效的.展开更多
Newton's method and generalized Newton's methods are efficient and convenient tools for constructing chaotic fractal images ,and have been widely investigated in recent years. This paper gives a class generali...Newton's method and generalized Newton's methods are efficient and convenient tools for constructing chaotic fractal images ,and have been widely investigated in recent years. This paper gives a class generalized Newton's methods that come from Secant method ,and constructs their chaotic fractal images to support the analyses of their algorithm.展开更多
文摘In this paper, we are going to present a class of nonlinear equation solving methods. Steffensen’s method is a simple method for solving a nonlinear equation. By using Steffensen’s method and by combining this method with it, we obtain a new method. It can be said that this method, due to not using the function derivative, would be a good method for solving the nonlinear equation compared to Newton’s method. Finally, we will see that Newton’s method and Steffensen’s hybrid method both have a two-order convergence.
文摘In this paper, a one-step Steffensen-type method with super-cubic convergence for solving nonlinear equations is suggested. The convergence order 3.383 is proved theoretically and demonstrated numerically. This super-cubic convergence is obtained by self-accelerating second-order Steffensen’s method twice with memory, but without any new function evaluations. The proposed method is very efficient and convenient, since it is still a derivative-free two-point method. Its theoretical results and high computational efficiency is confirmed by Numerical examples.
文摘In this paper, seven self-accelerating iterative methods with memory are derived from an optimal two-step Steffensen-type method without memory for solving nonlinear equations, their orders of convergence are proved to be increased,?numerical examples are demonstrat-ed demonstrated to verify the theoretical results, and applications for solving systems of nonlinear equations and BVPs of nonlinear ODEs are illustrated.
文摘研究了用Newton-Steffensen法求解非线性算子方程.当非线性算子F的一阶导数满足L-平均Lipschitz条件时,建立了Newton-Steffensen法的三阶收敛判据,同时也给出了收敛球半径的估计.作为应用,当F的一阶导数满足经典的Lipschitz条件时或F满足γ-条件时,建立了Newton-Steffensen法的三阶收敛判据及给出了收敛球半径的估计.从而推广了[Journal of Nonlinear and Convex Analysis,2018,19:433-460]中的相应结果.
文摘提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距离的线性组合,然后执行HDDI-FCM,在对数据集进行有效聚类的同时进行距离学习.为了保证迭代算法收敛,引入了Steffensen迭代法来改进计算簇中心点的迭代公式.讨论了算法中参数的选择.基于UCI(University of California,Irvine)数据集的实验结果表明该算法是有效的.
文摘Newton's method and generalized Newton's methods are efficient and convenient tools for constructing chaotic fractal images ,and have been widely investigated in recent years. This paper gives a class generalized Newton's methods that come from Secant method ,and constructs their chaotic fractal images to support the analyses of their algorithm.
