加速松驰迭代法的最优因子

本文定义了加速松驰迭代法的最优因子，并就具有相容次序且对角线上元素全不为零矩阵得到了最优因子的表达式，最后就本文的结论好于Ｇ．Ａｖｄｃｌａｓ＆Ａ．Ｈａｄｊｉｄｉｍｏｓ的结果给出了实例。     

线性方程组二级迭代法的收敛性

线性方程组二级迭代法的收敛性曹志浩（复旦大学）ＣＯＮＶＥＲＧＥＮＣＥＯＦＴＷＯ－ＳＴＡＧＥＩＴＥＲＡＴＩＶＥＭＥＴＨＯＤＳＦＯＲＴＨＥＳＯＬＵＴＩＯＮＯＦＬＩＮＥＡＲＳＹＳＴＥＭＳ￥ＣａｏＺｈｉ－ｂａｏ（ＦｕｄａｎＵｎｉｖｅｒｓｉｔｙ）Ａｂｓｔｒａｃ...     

关于混合型多项式Hammerstein积分方程正解迭代求法

本文给出了混合型多项式Ｈａｍｍｅｒｓｔｅｉｎ积分方程正解的迭代求法，并将所得结果应用到二阶非线性常微分方程的边值问题     

加速松弛迭代法的最优因子

本文定义了加速松弛迭代法的最优因子，并就具有相容次序且对角线上元素全不为零矩阵得到了最优因子的表达式，最后就本文的结论好于Ｇ．Ａｖｄｅｌａｓ＆Ａ．Ｈａｄｊｉｄｉｍｏｓ的结果给出了实例．     

并行矩阵多分裂块松弛迭代算法

并行矩阵多分裂块松弛迭代算法白中治（复旦大学数学研究所）ＰＡＲＡＬＬＥＬＭＡＴＲＩＸＭＵＬＴＩＳＰＬＩＴＴＩＮＧＢＬＯＣＫＲＥＬＡＸＡＴＩＯＮＩＴＥＲＡＴＩＯＮＭＥＴＨＯＤＳ￥ＢａｔＺｈｏｎｇ－ｚｈｉ（ＩｎｓｔｉｔｕｔｅｏｆＭａｔｈｅｍａｔｉｃｓ，Ｍ...     

DYNAMICS ON WEAKLY PSEUDOCONVEX DOMAINS

ＤＹＮＡＭＩＣＳＯＮＷＥＡＫＬＹＰＳＥＵＤＯＣＯＮＶＥＸＤＯＭＡＩＮＳ￥ＺＨＡＮＧＷＥＮＪＵＮ；ＲＥＮＦＹＵＡＯ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，ＨｅｎａｎＵｎｉｖｅｒｓｉｔｙｌＫａｉｆｅｎｇ４７５００１，Ｃｈｉｎａ．）（Ｉｎｓｔｉｔ...     

非线性方程组的具有单边初值条件的分裂型单调迭代法

本文利用区间迭代法的思想，提出了一种使用单边初值条件的分裂型单调迭代方法，证明了该方法的收敛性，并且具体化到常见的单调迭代法。     

解不适定算子方程的一个定常二步隐式迭代法

１．引言 设Ｘ,Ｙ是两个Ｈｉｌｂｅｒｔ空间,Ａ：Ｘ→Ｙ是有界线性算子,考虑算子方程 Ａｘ＝ｙ（１．１）如果Ａ的值域Ｒ（Ａ）在Ｙ中非闭,则方程（１．１）是不适定的［１］．许多应用科学中都归结出这一类方程,特别地,许多反问题是不适定的［２,３］．本文考虑方程（１．１）的 Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义解,这里Ａ是算子Ａ的Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义逆［１］．Ａ＋ｙ存在当且仅当本文均作这一假设．在实际中,通常代替（１．１）的是扰动方程这里右端项,为一给定的误差水平,Ｑ是Ｙ到Ｒ（Ａ）的正交投影算子．对扰…     

Hilbert尺度下求解非线性不适定问题的多水平迭代法

本文吸取了多水平方法的思想，采用多水平方法提供了离散化参数和迭代初值的合理的选择方法，提出了Hilbert尺度下求解非线性不适定问题的多水平Landweber迭代算法，并给出了算法的收敛性分析，证明了算法在整体上提高了Hilbert尺度下的Landweber迭代法的迭代效率。     

并行二级多分裂迭代方法

1．二级多分裂迭代法本义考虑求解线性代数方程组的几种同步与异步二级多分裂迭代法，其中A为nXn非奇异矩阵．多分别选代法考虑A的多种分裂用IF负对角权矩阵EI（ZEI一川进行组合，可得l＝1．多分裂迭代法任给刘始向量。0对k—1，2，…，直到收敛如果（1．2）中对所有l，MI＝Dilg（Al；…；A。。），EI＝（O，…，O，I，O，…，O），则多分裂迭代法退化成块Jacobi迭代法，同样，多分裂迭代法具有自然的并行性，若EI的某个对角元为0，则从的对应分量无需计算．当然，（1．3）可用直接法精确求解，如Gauss消去法，LU分解法等，但有…     

Piecewise shooting reproducing kernel method for linear singularly perturbed boundary value problems

In this letter, a new numerical method is proposed for solving second order linear singularly perturbed boundary value problems with left layers. Firstly a piecewise reproducing kernel method is proposed for second order linear singularly perturbed initial value problems. By combining the method and the shooting method, an effective numerical method is then proposed for solving second order linear singularly perturbed boundary value problems. Two numerical examples are used to show the effectiveness of the present method.     

A primal interior point method for the linear semidefinite programming problem

The linear semidefinite programming problem is examined. A primal interior point method is proposed to solve this problem. It extends the barrier-projection method used for linear programs. The basic properties of the proposed method are discussed, and its local convergence is proved.     

A multigrid compact finite difference method for solving the one‐dimensional nonlinear sine‐Gordon equation

The aim of this paper is to propose a multigrid method to obtain the numerical solution of the one‐dimensional nonlinear sine‐Gordon equation. The finite difference equations at all interior grid points form a large sparse linear system, which needs to be solved efficiently. The solution cost of this sparse linear system usually dominates the total cost of solving the discretized partial differential equation. The proposed method is based on applying a compact finite difference scheme of fourth‐order for discretizing the spatial derivative and the standard second‐order central finite difference method for the time derivative. The proposed method uses the Richardson extrapolation method in time variable. The obtained system has been solved by V‐cycle multigrid (VMG) method, where the VMG method is used for solving the large sparse linear systems. The numerical examples show the efficiency of this algorithm for solving the one‐dimensional sine‐Gordon equation. Copyright © 2014 John Wiley & Sons, Ltd.     

Circulant-block preconditioners for solving ordinary differential equations

Boundary value methods for solving ordinary differential equations require the solution of non-symmetric, large and sparse linear systems. In this paper, these systems are solved by using the generalized minimal residual (GMRES) method. A circulant-block preconditioner is proposed to speed up the convergence rate of the GMRES method. Theoretical and practical arguments are given to show that this preconditioner is more efficient than some other circulant-type preconditioners in some cases. A class of waveform relaxation methods is also proposed to solve the linear systems.     

一类Riccati矩阵方程广义自反解的双迭代算法

本文研究了一类Riccati矩阵方程广义自反解的数值计算问题.利用牛顿算法将Riccati矩阵方程的广义自反解问题转化为线性矩阵方程的广义自反解或者广义自反最小二乘解问题,再利用修正共轭梯度法计算后一问题,获得了求Riccati矩阵方程的广义自反解的双迭代算法.拓宽了求解非线性矩阵方程的迭代算法.数值算例表明双迭代算法是有效的.     

Complexity of the Gravitational Method for Linear Programming

In the gravitational method for linear programming, a particle is dropped from an interior point of the polyhedron and is allowed to move under the influence of a gravitational field parallel to the objective function direction. Once the particle falls onto the boundary of the polyhedron, its subsequent motion is constrained to be on the surface of the polyhedron with the particle moving along the steepest-descent feasible direction at any instant. Since an optimal vertex minimizes the gravitational potential, computing the trajectory of the particle yields an optimal solution to the linear program.Since the particle is not constrained to move along the edges of the polyhedron, as the simplex method does, the gravitational method seemed to have the promise of being theoretically more efficient than the simplex method. In this paper, we first show that, if the particle has zero diameter, then the worst-case time complexity of the gravitational method is exponential in the size of the input linear program. As a simple corollary of the preceding result, it follows that, even when the particle has a fixed nonzero diameter, the gravitational method has exponential time complexity. The complexity of the version of the gravitational method in which the particle diameter decreases as the algorithm progresses remains an open question.     

广义交替近似梯度算法的线性收敛分析

针对两个可分凸函数的和在线性约束下的极小化问题,在交替方向法的框架下,提出广义的交替近似梯度算法.在一定的条件下,该算法具有全局及线性收敛性.数值实验表明该算法有好的数值表现.     

On the Equivalence of the Simplex Methods and a Multiplier-Alike Method for Linear Programming

In linear programming, the simplex method has been viewed for a long time as an efficient tool. Interior methods have attracted a lot of attention since they were proposed recently. It seems plausible intuitively that there is no reason why a good linear programming algorithm should not be allowed to cross the boundary of the feasible region when necessary. However, such an algorithm is seldom studied. In this paper, we will develop first a framework of a multiplier-alike algorithm for linear programming which allows its trajectory to move across the boundary of the feasible region. Second, we illustrate that such a framework has the potential to perform as well as the simplex method by showing that these methods are equivalent in a well-defined sense, even though they look so different.     

A Two-Level Method for Nonsymmetric Eigenvalue Problems

A two-level discretization method for eigenvalue problems is studied. Compared to the standard Galerkin finite element discretization technique performed on a fine grid this method discretizes the eigenvalue problem on a coarse grid and obtains an improved eigenvector (eigenvalue) approximation by solving only a linear problem on the fine grid (or two linear problems for the case of eigenvalue approximation of nonsymmetric problems). The improved solution has the asymptotic accuracy of the Galerkin discretization solution. The link between the method and the iterated Galerkin method is established. Error estimates for the general nonsymmetric case are derived.     

Application of homotopy perturbation methods for solving systems of linear equations

In this paper, homotopy perturbation methods (HPMs) are applied to obtain the solution of linear systems, and conditions are deduced to check the convergence of the homotopy series. Moreover, we have adapted the Richardson method, the Jacobi method, and the Gauss-Seidel method to choose the splitting matrix. The numerical results indicate that the homotopy series converges much more rapidly than the direct methods for large sparse linear systems with a small spectrum radius.