广义TOR方法及其收敛性

本文定义了广义的TOR迭代法,并且给出了广义TOR方法的Stein-Rosenberg型定理,讨论了广义TOR方法的单调收敛性.     

滞后型脉冲泛函微分方程周期解的存在性

本文借助Mawhin重合度理论中的延拓定理和广义常微分方程周期解的存在性,在滞后型脉冲泛函微分方程与广义常微分方程存在等价关系的条件下,建立了滞后型脉冲泛函微分方程周期解的存在性定理.     

关于Newton法的Kantorovich型定理及其优函数

本文研究Newton法的Kantorovich型定理的特点及其对Newton法的半局部收敛性研究的思想方法,论述广义Lipschitz条件下的Kantorovich型定理的概括性和统一性.同时,在理论上当x_0取定时,针对每一个满足广义Lipschitz条件的光滑算子,给出优函数的一个构造方法.     

完备广义度量空间F压缩不动点定理（英文）

本文研究了广义度量空间(A)型和(B)型弱F压缩的问题.利用迭代的方法,获得了在完备广义度量空间关于这些映射的不动点定理的结果,推广了完备度量空间F压缩的一些结果.     

广义L-KKM定理及其应用

引入广义L-KKM映射的概念,它包含R-KKM映射,G-KKM映射,H-KKM映射为其特例.在具有(H)性质的拓扑空间中证明了一些新的广义L-KKM型定理,并进一步获得了关于开覆盖的匹配定理.作为广义L-KKM型定理应用,证明了非空交定理.     

在满足广义指数型二分性条件下的线性化

本文将Ｈａｒｔｍａｎ线性化定理和Ｐａｌｍｅｒ线性化定理推广到具有广义指数型二分性的系统,证明了等价函数的强一致连续性;作为应用,在例子中给出了一类系 统的稳定性的判别方法．     

广义凸空间内的KKM型定理和极小极大不等式及鞍点定理

本文在非紧G-凸空间内对具有有限闭值和有限开值的G-KKM,广义G-KKM和广义S-KKM映象建立了某些新的KKM型定理。应用这些KKM型定理,在G-凸空间内得到了新的 Ky Fan型极小极大不等式和鞍点定理。这些结论推广了最近文献中的许多已有结果。     

完备的$\nu$-广义度量空间上的不动点定理[英文]

本文对度量空间中$C$类函数的压缩映射进行推广. 在完备的$\nu$-广义度量空间上, 利用构造迭代序列的方法, 证明了关于($\psi$,$\phi$)-类型压缩映射的不动点定理. 并且证明了广义的$F$类型压缩和广义$\theta$类型压缩映射.     

广义Laguerre级数的奇点和过度收敛

本文在§1中推广了古典正规发散的概念,给出了广义Laguerre级数在收敛抛物线上的奇点的判断定理.在§2中给出了广义Laguerre级数的“Ostrowski”型的过度收敛定理.     

完备的ν-广义度量空间上的不动点定理（英文）

本文对度量空间中C类函数的压缩映射进行推广.在完备的ν-广义度量空间上,利用构造迭代序列的方法,证明了关于(ψ,?)-类型压缩映射的不动点定理.并且证明了广义的F类型压缩和广义θ类型压缩映射.     

The Generalized Forward-Backward Splitting Method for the Minimization of the Sum of Two Functions in Banach Spaces

We propose a convergence analysis of the generalized forward-backward splitting iterative procedure for the minimization of the sum of two functions in Banach spaces. The generalized forward-backward splitting method is applied to the minimization of functionals of the Tikhonov type with semi-norm regularization. We also investigate the perturbed forward-backward splitting method and prove its stability.     

A new Type of Matrix Splitting and its Applications

A possible type of the matrix splitting is introduced. Using this matrix splitting, we introduce a few properties and representations of generalized inverses as well as iterative methods for computing various solutions of singu- lar linear systems. This matrix splitting is a generalization of the known index splitting from [13] and a proper splitting from [4]. Using a generalization of the condition number and introduced representations of generalized inverses, we ob- tain several norm estimates.     

Generalized skew‐Hermitian triangular splitting iteration methods for saddle‐point linear systems

A generalized skew‐Hermitian triangular splitting iteration method is presented for solving non‐Hermitian linear systems with strong skew‐Hermitian parts. We study the convergence of the generalized skew‐Hermitian triangular splitting iteration methods for non‐Hermitian positive definite linear systems, as well as spectrum distribution of the preconditioned matrix with respect to the preconditioner induced from the generalized skew‐Hermitian triangular splitting. Then the generalized skew‐Hermitian triangular splitting iteration method is applied to non‐Hermitian positive semidefinite saddle‐point linear systems, and we prove its convergence under suitable restrictions on the iteration parameters. By specially choosing the values of the iteration parameters, we obtain a few of the existing iteration methods in the literature. Numerical results show that the generalized skew‐Hermitian triangular splitting iteration methods are effective for solving non‐Hermitian saddle‐point linear systems with strong skew‐Hermitian parts. Copyright © 2013 John Wiley & Sons, Ltd.     

Multistep matrix splitting iteration preconditioning for singular linear systems

Multistep matrix splitting iterations serve as preconditioning for Krylov subspace methods for solving singular linear systems. The preconditioner is applied to the generalized minimal residual (GMRES) method and the flexible GMRES (FGMRES) method. We present theoretical and practical justifications for using this approach. Numerical experiments show that the multistep generalized shifted splitting (GSS) and Hermitian and skew-Hermitian splitting (HSS) iteration preconditioning are more robust and efficient compared to standard preconditioners for some test problems of large sparse singular linear systems.     

A few splitting criteria for vector bundles

We prove a few splitting criteria for vector bundles on a quadric hypersurface and Grassmannians. We give also some cohomological splitting conditions for rank 2 bundles on multiprojective spaces. The tools are monads and a Beilinson’s type spectral sequence generalized by Costa and Miró-Roig.      

PICARD ITERATION FOR NONSMOOTH EQUATIONS

1. IntroductionConsider the following nonsmooth equationsF(x) = 0 (l)where F: R" - R" is LipsChitz continuous. A lot of work has been done and is bellg doneto deal with (1). It is basicly a genera1ization of the cIassic Newton method [8,10,11,14],Newton-lthe methods[1,18] and quasiNewton methods [6,7]. As it is discussed in [7], the latter,quasiNewton methods, seem to be lindted when aPplied to nonsmooth caJse in that a boundof the deterioration of uPdating matrir can not be maintained w…     

A wider convergence area for the MSTMAOR iteration methods for LCP

In this paper, based on the Hermitian and skew-Hermitian splitting, we give a generalized modified Hermitian and skew-Hermitian splitting (GMHSS) method to solve singular complex symmetric linear systems, this method has two parameters. We give the semi-convergent conditions, and some numerical experiments are given to illustrate the efficiency of this method.     

A generalized preconditioned HSS method for non-Hermitian positive definite linear systems

Based on the HSS (Hermitian and skew-Hermitian splitting) and preconditioned HSS methods, we will present a generalized preconditioned HSS method for the large sparse non-Hermitian positive definite linear system. Our method is essentially a two-parameter iteration which can extend the possibility to optimize the iterative process. The iterative sequence produced by our generalized preconditioned HSS method can be proven to be convergent to the unique solution of the linear system. An exact parameter region of convergence for the method is strictly proved. A minimum value for the upper bound of the iterative spectrum is derived, which is relevant to the eigensystem of the products formed by inverse preconditioner and splitting. An efficient preconditioner based on incremental unknowns is presented for the actual implementation of the new method. The optimality and efficiency are effectively testified by some comparisons with numerical results.     

The generalized HSS method for solving singular linear systems

For the singular, non-Hermitian, and positive semidefinite linear systems, we propose an alternating-direction iterative method with two parameters based on the Hermitian and skew-Hermitian splitting. The semi-convergence analysis and the quasi-optimal parameters of the proposed method are discussed. Moreover, the corresponding preconditioner based on the splitting is given to improve the semi-convergence rate of the GMRES method. Numerical examples are given to illustrate the theoretical results and the efficiency of the generalized HSS method either as a solver or a preconditioner for GMRES.     

Semiconvergence of extrapolated iterative methods for singular linear systems

In this paper, we discuss convergence of the extrapolated iterative methods for solving singular linear systems. A general principle of extrapolation is presented. The semiconvergence of an extrapolated method induced by a regular splitting and a nonnegative splitting is proved whenever the coefficient matrix A is a singular M-matrix with ‘property c’ and an irreducible singular M-matrix, respectively. Since the (generalized, block) JOR and AOR methods are respectively the extrapolated methods of the (generalized, block) Jacobi and SOR methods, so the semiconvergence of the (generalized, block) JOR and AOR methods for solving general singular systems are proved. Furthermore, the semiconvergence of the extrapolated power method, the (block) JOR, AOR and SOR methods for solving Markov chains are discussed.