关于Katz指标的二级分裂迭代方法

本文研究复杂网络中计算Katz指标的迭代法,基于网络拓扑结构,在快速Katz指标算法的基础上,运用二级分裂迭代思想,提出了具有两个参数的二级分裂迭代法,并研究了该方法的收敛性.基于该方法的收缩因子的计算公式,讨论了迭代参数可能的选择,通过参数的选择能有效提高二级迭代法的收敛效率.最后通过数值实例验证了此方法的有效性.     

关于PageRank的广义二级分裂迭代方法

本文研究计算PageRank的迭代法,在Gleich等人提出的内/外迭代方法的基础上,提出了具有三个参数的广义二级分裂迭代法,该方法包含了内/外迭代法和幂迭代法,并研究了该方法的收敛性.基于该方法的收缩因子的计算公式,讨论了迭代参数可能的选择,通过参数的选择能有效提高内/外迭代法的收敛效率.     

关于鞍点问题的预处理HSS-SOR交替分裂迭代方法

为了高效地求解大型稀疏鞍点问题,在白中治,Golub和潘建瑜提出的预处理对称/反对称分裂(PHss)迭代法的基础上,通过结合SOR-like迭代格式对原有迭代算法进行加速,提出了一种预处理HSS-SOR交替分裂迭代方法,并研究了该算法的收敛性.数值例子表明:通过参数值的选择,新算法比SOR-like和PHSS算法都具有更快的收敛速度和更少的迭代次数,选择了合适的参数值后,可以提高算法的收敛效率.     

关于鞍点问题的广义预处理HSS-SOR交替分裂迭代方法

本文研究了鞍点问题的迭代法. 在白中治,Golub和潘建瑜提出的预处理对称/反对称分裂（PHSS）迭代法的基础上,通过结合GSOR迭代格式,利用两个参数加速,提出了一种广义预处理HSS-SOR交替分裂迭代法,并研究了该方法的收敛性.数值结果表明本文所给方法是有效的.     

不用求导含参数的三阶收敛迭代方法

基于弦截法和Steffensen迭代法,本文提出了求解非线性方程f(x)=0的两种带有参数的迭代算法,这两种算法不带有导数,且经过收敛性分析证明至少是三阶收敛的,最后用数值试验验证了本文两种迭代算法的有效性和优越性.     

两类五阶解非线性方程组的迭代算法

本文首先根据Runge-Kutta方法的思想,结合Newton迭代法,提出了一类带参数的解非线性方程组F(x)=0的迭代算法,然后基于解非线性方程f(x)=0的King算法,给出第二类解非线性方程组的迭代算法,收敛性分析表明这两类算法都是五阶收敛的.其次给出了本文两类算法的效率指数,以及一些已知算法的效率指数,并且将本文算法的效率指数与其它方法进行详细的比较,通过效率比率R_(i,j)可知本文算法具有较高的计算效率.最后给出了四个数值实例,将本文两类算法与现有的几种算法进行比较,实验结果说明本文算法收敛速度快,迭代次数少,有明显的优势.     

具有参数平方收敛的线性插值迭代类

提出了一类具有参数平方收敛的求解非线性方程的线性插值迭代法,方法以Newton法和Steffensen法为其特例,并且给出了该类方法的最佳迭代参数.数值试验表明,选用最佳迭代参数或其近似值的新方法比Newton法和Steffensen方法更有效.     

求解PageRank问题的多步幂法修正的内外迭代法

引用两种加速计算PageRank的算法,分别为内外迭代法和两步分裂迭代算法.从这两种方法中,得到多步幂法修正的内外迭代方法.首先,详细介绍了算法实施过程.然后,对此算法的收敛性进行证明,并且将此算法的谱半径与两步分裂迭代算法的谱半径进行比较.最后,数值试验说明该算法的计算速度比两步分裂迭代法要快.     

基于分数阶扩散方程的离散线性代数方程组迭代方法研究

本文构建一类双参数拟Toeplitz分裂（TQTS）迭代方法求解变系数非定常空间分数阶扩散方程.TQTS迭代法是基于QTS迭代法引入双参技术建立而成,通过选取适当的参数使迭代矩阵谱半径变得更小,从而有效提升收敛的速度.然后对TQTS迭代法进行收敛性分析,获得相应的收敛区域,并对迭代法中涉及的参数进行讨论,获得使迭代矩阵谱半径上界达到最小的最优参数的表达式.最后通过数值仿真实验验证TQTS迭代法的有效性,实验结果表明TQTS迭代法改进效果十分突出,在迭代时间和步数上均有明显的减小.     

非Hermitian正定线性方程组的外推的HSS迭代方法

为了高效地求解大型稀疏非Hermitian正定线性方程组，在白中治、Golub和Ng提出的Hermitian和反Hermitian分裂（HSS）迭代法的基础上，通过引入新的参数并结合迭代法的松弛技术，对HSS迭代方法进行加速，提出了一种新的外推的HSS迭代方法（EHSS），并研究了该方法的收敛性.数值例子表明：通过参数值的选择，新方法比HSS方法具有更快的收敛速度和更少的迭代次数，选择了合适的参数值后，可以提高HSS方法的收敛效率.     

SOME LEAST SQUARES ESTIMATES OF THE AUTOREGRESSIVE MODELS

In this paper, we present some iterative methods for solving lth order autoregressive models, prove global convergence for l=1 case, and the numerical results of new algorithms seem to be more efficient than the ones of Cochrane-Orcutt iterative method.     

New algorithms for the split variational inclusion problems and application to split feasibility problems

ABSTRACT

In this paper, we suggest two new iterative methods for finding an element of the solution set of split variational inclusion problem in real Hilbert spaces. Under suitable conditions, we present weak and strong convergence theorems for these methods. We also apply the proposed algorithms to study the split feasibility problem. Finally, we give some numerical results which show that our proposed algorithms are efficient and implementable from the numerical point of view.     

Existence and iterative approximations of solutions for mixed quasi-variational-like inequalities in Banach spaces

In this paper, we introduce and investigate a new class of mixed quasi-variational-like inequalities in reflexive Banach spaces. By applying a minimax inequality due to Ding–Tan and a lemma due to Chang, we establish some existence and uniqueness results of solution for the mixed quasi-variational-like inequality. Next, by using a KKM theorem due to Fan and an auxiliary principle technique due to Cohen, we suggest two iterative algorithms and study the convergence criteria of iterative sequences generated by the iterative algorithms. Our results extend, improve and unify several known results in the literature.     

Iterative algorithms with variable anchors for non-expansive mappings

In this paper, we suggest and analyze some new iterative algorithms with variable anchors for non-expansive mapping in Banach spaces. We prove that the proposed iterative algorithms converge strongly to a fixed point of some non-expansive mapping. We also obtain some corollaries which include some results as special cases. Furthermore, we conclude the strong convergence of the so-called viscosity iterative algorithms.     

The Gauss-Newton Methods via Conjugate Gradient Path without Line Search Technique for Solving Nonlinear Systems

In this article, we propose the Gauss-Newton methods via conjugate gradient path for solving nonlinear systems. By constructing and solving a linearized model of the nonlinear systems, we obtain the iterative direction by employing the conjugate gradient path. In successive iterations, the approximate Jacobian of the nonlinear systems is updated by a Broyden formula to construct the conjugate path. The global convergence and local superlinear convergence rate of the proposed algorithms are established under some reasonable conditions. Finally, the numerical results are reported to show the effectiveness of the proposed algorithms.     

Completely generalized mildly nonlinear complementarity problems for fuzzy mappings

In this paper, we introduce and study a new class of completely generalized mildly nonlinear complementarity problems for fuzzy mappings and construct some new iterative algorithms. We also show the existence of solution and the convergence of iterative sequences generated by the algorithms. Our results extend some recent results of Noor, Chang and Huang.     

Accelerated hybrid and shrinking projection methods for variational inequality problems

ABSTRACT

In this paper, we introduce several new extragradient-like approximation methods for solving variational inequalities in Hilbert spaces. Our algorithms are based on Tseng's extragradient method, subgradient extragradient method, inertial method, hybrid projection method and shrinking projection method. Strong convergence theorems are established under appropriate conditions. Our results extend and improve some related results in the literature. In addition, the efficiency of our algorithms is shown through numerical examples which are defined by the hybrid projection methods.     

Iterative methods for solving proximal split minimization problems

In this paper, we propose two iterative algorithms for finding the minimum-norm solution of a split minimization problem. We prove strong convergence of the sequences generated by the proposed algorithms. The iterative schemes are proposed in such a way that the selection of the step-sizes does not need any prior information about the operator norm. We further give some examples to numerically verify the efficiency and implementation of our new methods and compare the two algorithms presented. Our results act as supplements to several recent important results in this area.