无约束优化问题的精细修正牛顿算法

针对牛顿法在求解一般非凸函数极小值过程中,迭代点处Hessian矩阵不一定正定的情况,提出了一种精细修正的牛顿法.该方法充分利用迭代点处目标函数的一阶、二阶信息,合适选取搜索方向,是最速下降法、牛顿法和已有修正牛顿法相混合的一种方法.在较弱的条件下建立了算法的全局收敛性.进一步的数值实验验证了提出的算法比以往同类算法计...     

非对称线性方程组的二阶段分裂迭代法

本文针对非对称正定矩阵提出了一个收敛分裂, 给出了分裂收敛的充要条件. 在此基础上, 提出系数为非对称正定矩阵的线性方程组的二阶段算法, 并讨论了算法的收敛条件. 最后, 通过数值例子展示了算法的有效性.     

解凸约束非线性单调方程组的无导数低存储Broyden族投影法

拟牛顿法是求解非线性方程组的一类有效方法.相较于经典的牛顿法,拟牛顿法不需要计算Jacobian矩阵且仍具有超线性收敛性.本文基于BFGS和DFP的迭代公式,构造了新的充分下降方向.将该搜索方向和投影技术相结合,本文提出了无导数低存储的投影算法求解带凸约束的非线性单调方程组并证明了该算法是全局且R-线性收敛的.最后,将该算法用于求解压缩感知问题.实验结果表明,本文所提出的算法具有良好的计算效率和稳定性.     

并行区间矩阵多分裂AOR算法及其收敛性

本文提出了一类求解大型区间线性方程组的并行区间矩阵多分裂松弛算法,并在系数矩阵是区间H-矩阵的条件下,建立了这类算法的收敛理论。     

求解增广线性系统的局部多分裂迭代法

为了在高性能计算机上求解增广线性系统,基于并行多分裂的两种技巧,本文提出一种局部多分裂迭代格式,给出当增广线性系统的矩阵为M-矩阵和H-矩阵时新方法的收敛性理论.并讨论预条件矩阵的特征值情形.     

H-矩阵线性方程组的一类预条件并行多分裂SOR迭代法

基于并行多分裂算法的思想及SOR迭代格式, 本文提出一种求解H-矩阵线性方程组新的并行多分裂SOR迭代法, 新方法某种程度上避免了SOR迭代法中选取最优参数的困难. 同时, 选取Kohno等(1997)提出的预条件子$P=I+S_{\alpha}$对原始线性方程组进行预处理, 进而给出了一种实用的预条件并行多分裂SOR迭代法. 理论分析和数值实验均表明, 新算法是实用而有效的.     

迭代求解非Hermitian正定线性方程组的衍生多分裂方法[英文]

本文研究迭代求解非Hermitian正定线性方程组的问题.在系数矩阵HS分裂的基础上,提出了一种新的衍生并行多分裂迭代方法.通过参数调节分配反Hermitian部分给Hermitian部分的多分裂来衍生出非Hermitian正定系数矩阵的并行多分裂迭代格式,并利用优化技巧来获得权矩阵.同时,建立算法的收敛理论.最后用数值实验表明了新方法的有效性和可行性.     

求解一类隐式互补问题的加速模系矩阵分裂迭代方法

本文提出求解一类隐式互补问题的加速模系矩阵分裂迭代法.通过将隐式互补问题重新表述为一个等价的不动点方程,建立一类新的基于模系的两步矩阵分裂方法,并在一定条件下证明了方法的收敛性.数值实验表明,该方法在迭代步数上优于传统的模系矩阵分裂迭代方法.     

一类求解双边障碍问题的松弛型二级多分裂并行算法

本文研究了一类求解双障碍问题的松弛型二级多分裂并行算法.运用矩阵多分裂理论,在一定条件下证明了算法的收敛性.数值算例说明算法是有效的和稳健的.     

无约束优化问题的对角稀疏拟牛顿法

对无约束优化问题提出了对角稀疏拟牛顿法,该算法采用了Armijo非精确线性搜索,并在每次迭代中利用对角矩阵近似拟牛顿法中的校正矩阵,使计算搜索方向的存贮量和工作量明显减少,为大型无约束优化问题的求解提供了新的思路．在通常的假设条件下,证明了算法的全局收敛性,线性收敛速度并分析了超线性收敛特征。数值实验表明算法比共轭梯度法有效,适于求解大型无约束优化问题．     

非对称线性互补问题的并行二级多分裂迭代法

提出了求解非对称线性互补问题的并行二级多分裂迭代算法,并证明了该算法的收敛性,最后通过数值实验验证了算法的有效性和可行性.     

线性互补问题的并行多分裂松弛迭代算法

运用矩阵多重分裂理论,同时考虑并行计算与松弛迭代法,得到一类求解线性互补问题的高效数值算法．当问题的系数矩阵为对角元为正的H-矩阵或对称半正定矩阵时,证明了算法的全局收敛性；该算法与已有算法相比,具有计算量小、计算速度快等特点,因而特别适于求解大规模问题．数值试验的结果说明了算法的有效性．     

求解PageRank问题的重启GMRES修正的多分裂迭代法

PageRank算法已经成为网络搜索引擎的核心技术。针对PageRank问题导出的线性方程组,首先将Krylov子空间方法中的重启GMRES(generalized minimal residual)方法与多分裂迭代(multi-splitting iteration,MSI)方法相结合,提出了一种重启GMRES修正的多分裂迭代法;然后,给出了该算法的详细计算流程和收敛性分析;最后,通过数值实验验证了该算法的有效性。     

A class of smoothing functions for nonlinear and mixed complementarity problems

We propose a class of parametric smooth functions that approximate the fundamental plus function, (x)₊=max{0, x}, by twice integrating a probability density function. This leads to classes of smooth parametric nonlinear equation approximations of nonlinear and mixed complementarity problems (NCPs and MCPs). For any solvable NCP or MCP, existence of an arbitrarily accurate solution to the smooth nonlinear equations as well as the NCP or MCP, is established for sufficiently large value of a smoothing parameter . Newton-based algorithms are proposed for the smooth problem. For strongly monotone NCPs, global convergence and local quadratic convergence are established. For solvable monotone NCPs, each accumulation point of the proposed algorithms solves the smooth problem. Exact solutions of our smooth nonlinear equation for various values of the parameter , generate an interior path, which is different from the central path for interior point method. Computational results for 52 test problems compare favorably with these for another Newton-based method. The smooth technique is capable of solving efficiently the test problems solved by Dirkse and Ferris [6], Harker and Xiao [11] and Pang & Gabriel [28].This material is based on research supported by Air Force Office of Scientific Research Grant F49620-94-1-0036 and National Science Foundation Grant CCR-9322479.     

一类奇性积分方程的多尺度配置快速算法

This paper develops fast multiscale collocation methods for a class of Fredholm integral equations of the second kind with singular kernels. A truncation strategy for the coefficient matrix of the corresponding discrete system is proposed, which forms a basis for fast algorithms. The convergence, stability and computational complexity of these algorithms are analyzed.     

Convergence of a generalized Newton and an inexact generalized Newton algorithms for solving nonlinear equations with nondifferentiable terms

In this paper, we consider two versions of the Newton-type method for solving a nonlinear equations with nondifferentiable terms, which uses as iteration matrices, any matrix from B-differential of semismooth terms. Local and global convergence theorems for the generalized Newton and inexact generalized Newton method are proved. Linear convergence of the algorithms is obtained under very mild assumptions. The superlinear convergence holds under some conditions imposed on both terms of equation. Some numerical results indicate that both algorithms works quite well in practice.      

求解低秩矩阵恢复问题的非单调交替梯度方向法

低秩矩阵恢复问题作为一类在图像处理和信号数据分析等领域中都十分重要的问题已被广泛研究.本文在交替方向算法的框架下,应用非单调技术,提出一种求解低秩矩阵恢复问题的新算法.该算法在每一步迭代过程中,首先利用一步带有变步长梯度算法同时更新低秩部分的两块变量,然后采用非单调技术更新稀疏部分的变量.在一定的假设条件下,本文证明了...     

On a new family of high‐order iterative methods for the matrix pth root

The main goal of this paper is to approximate the principal pth root of a matrix by using a family of high‐order iterative methods. We analyse the semi‐local convergence and the speed of convergence of these methods. Concerning stability, it is well known that even the simplified Newton method is unstable. Despite it, we present stable versions of our family of algorithms. We test numerically the methods: we check the numerical robustness and stability by considering matrices that are close to be singular and badly conditioned. We find algorithms of the family with better numerical behavior than the Newton and the Halley methods. These two algorithms are basically the iterative methods proposed in the literature to solve this problem. Copyright © 2015 John Wiley & Sons, Ltd.     

The relaxed inexact projection methods for the split feasibility problem

In this paper we present several relaxed inexact projection methods for the split feasibility problem (SFP). Each iteration of the first proposed algorithm consists of a projection onto a halfspace containing the given closed convex set. The algorithm can be implemented easily and its global convergence to the solution can be established under suitable conditions. Moreover,we present some modifications of the relaxed inexact projection method with constant stepsize by adopting Armijo-like search. We furthermore present a variable-step relaxed inexact projection method which does not require the computation of the matrix inverses and the largest eigenvalue of the matrix A^TA, and the objective function can decrease sufficiently at each iteration. We show convergence of these modified algorithms under mild conditions. Finally, we perform some numerical experiments, which show the behavior of the algorithms proposed.