类Broyden族非拟牛顿算法对一般目标函数的全局收敛性

应用双参数的类Broyden族校正公式,为研究求解无约束最优化问题的拟牛顿类算法对一般目标函数的收敛性这个开问题提供了一种新的方法．     

贯序和并行求解线性与非线性方程组的一类算法

1.引论 Abaffy,Broyden和spedicato在最近的论文中,提出了一类求解线性和非线性方程组的算法(有可能推广于求解其它问题,例如最优化问题).我们首先给出这类算法求解线性方程组时的基本形式.设线性方程组为 或把它写成矩阵形式 其中A=(a_1,…,a_m)是n×m阶矩阵,共秩q可以小于m.算法具有拟Newton型结构,其计算步骤如下:     

Broyden算法类中两个新的开关算法

<正> 本文从变分的角度,对求解无约束最优化问题 minf(x)x∈R~n给出了Broyden算法中两个新的开关算法。在Wolfe不精确线性搜索的准则下,证明了它们具有全局收敛性,并对超线性收敛进行探讨。计算实例表明,新算法是有效的。     

一类不精确拟牛顿型算法的局部收敛性分析

为了求解Hilbert空间中算子方程或minimax问题,构造了一类无穷维空间中的不精确拟牛顿算法,并考虑了其线性收敛性和超线性收敛性,是对有限维空间中不精确拟牛顿法的推广.当迭代算子由Broyden修正给出时,在一定的假设条件下,得到了不精确Broyden方法的线性收敛性和超线性收敛性.这为使用不精确拟牛顿法结合投影法求解算子方程做好了准备.     

一类求解非凸函数极小的修正Broyden算法

本文研究了求解非凸函数极小的数值方法,提出了一类求解非凸函数极小的修正Broyden算法,并证明了所提出的修正Broyden算法是全局收敛和q-超线性收敛的.     

再论Broyden方法的收敛性

本文用 Smale 提出的点估计理论,建立了在点估计条件下的求解非线性方程组 F(x)=0的著名的 Broyden 方法的收敛性及解的存在唯一性定理.从而在 R~n 空间的解析映射类上,解除了由于 F′的区域性 Lipschitz 条件带来的 Broyden 方法收敛判据之间的相互制约性.它为一大类修正算法点估计理论的建立,提供了新的途径.     

再论Broyden方法的收敛性

本文用Smale提出的点估计理论,建立了在点估计条件下的求解非线性方程组F(x)=0的著名的Broyden方法的收敛性及解的存在唯一性定理,从而在R~n空间的解析映射类上,解除了由于F′的区域性Lipschitz条件带来的Broyden方法收敛判据之间的相互制约性。它为一大类修正算法点估计理论的建立,提供了新的途径。     

δ~2-加速的Broyden计算格式

本文对于求解非线性方程组 F (x) =0的 Broyden秩 1第二种方法的计算格式进行修正 ,在算法实现过程中使用了δ2 -加速技巧 ,从而大大提高了算法的收敛速度 .     

Broyden修正算法

对于求解非线性方程组F (x) =0的Broyden秩1方法的计算格式提出一种修正算法,尝试利用矩阵的奇异值分解求解迭代方程组,并且配合使用加速技巧,从而大大提高了算法的安全性和收敛速度.数值算例表明了新算法的有效性.     

m步非线性多重分裂Newton法的收敛性

本文讨论了多重分裂算法在求解一类非线性方程组的全局收敛性和单侧收敛性．当用研步Newton法来代替求得每个非线性多重分裂子问题的近似解时，同样给出相应收敛性结论．数值算例证实了算法的有效性．     

A parallel descent algorithm for convex programming

In this paper, we propose a parallel decomposition algorithm for solving a class of convex optimization problems, which is broad enough to contain ordinary convex programming problems with a strongly convex objective function. The algorithm is a variant of the trust region method applied to the Fenchel dual of the given problem. We prove global convergence of the algorithm and report some computational experience with the proposed algorithm on the Connection Machine Model CM-5.     

Solving a Class of Linearly Constrained Indefinite Quadratic Problems by D.C. Algorithms

Linearly constrained indefinite quadratic problems play an important role in global optimization. In this paper we study d.c. theory and its local approachto such problems. The new algorithm, CDA, efficiently produces local optima and sometimes produces global optima. We also propose a decomposition branch andbound method for globally solving these problems. Finally many numericalsimulations are reported.     

Nonlinear Proximal Decomposition Method for Convex Programming

In this paper, we propose a new decomposition method for solving convex programming problems with separable structure. The proposed method is based on the decomposition method proposed by Chen and Teboulle and the nonlinear proximal point algorithm using the Bregman function. An advantage of the proposed method is that, by a suitable choice of the Bregman function, each subproblem becomes essentially the unconstrained minimization of a finite-valued convex function. Under appropriate assumptions, the method is globally convergent to a solution of the problem.     

Entropic proximal decomposition methods for convex programs and variational inequalities

We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmic-quadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the first time provably convergent decomposition schemes based on C ^∞ Lagrangians for solving convex structured problems. Under the only assumption that the primal-dual problems have nonempty solution sets, global convergence of the primal-dual sequences produced by the algorithm is established. Received: October 6, 1999 / Accepted: February 2001?Published online September 17, 2001     

A new modified Adomian decomposition method and its multistage form for solving nonlinear boundary value problems with Robin boundary conditions

In this paper we propose a new modified recursion scheme for the resolution of boundary value problems (BVPs) for second-order nonlinear ordinary differential equations with Robin boundary conditions by the Adomian decomposition method (ADM). Our modified recursion scheme does not incorporate any undetermined coefficients. We also develop the multistage ADM for BVPs encompassing more general boundary conditions, including Neumann boundary conditions.     

Alternating oblique projections for coupled linear systems

In this work we propose the use of alternating oblique projections (AOP) for the solution of the saddle points systems resulting from the discretization of domain decomposition problems. These systems are called coupled linear systems. The AOP method is a descent method in which the descent direction is defined by using alternating oblique projections onto the search subspaces. We prove that this method is a preconditioned simple gradient (Uzawa) method with a particular preconditioner. Finally, a preconditioned conjugate gradient based version of AOP is proposed. AMS subject classification 65F10, 65N22, 65Y05     

A feasible decomposition method for constrained equations and its application to complementarity problems

In this paper, by analyzing the propositions of solution of the convex quadratic programming with nonnegative constraints, we propose a feasible decomposition method for constrained equations. Under mild conditions, the global convergence can be obtained. The method is applied to the complementary problems. Numerical results are also given to show the efficiency of the proposed method.     

On the Adomian Decomp osition Metho d for Solving PDEs

In this paper, we explore some issues related to adopting the Adomian decomposition method (ADM) to solve partial differential equations (PDEs), par-ticularly linear diffusion equations. Through a proposition, we show that extending the ADM from ODEs to PDEs poses some strong requirements on the initial and boundary conditions, which quite often are violated for problems encountered in en-gineering, physics and applied mathematics. We then propose a modified approach, based on combining the ADM with the Fourier series decomposition, to provide solu-tions for those problems when these conditions are not met. In passing, we shall also present an argument that would address a long-term standing“pitfall”of the original ADM and make this powerful approach much more rigorous in its setup. Numeri-cal examples are provided to show that our modified approach can be used to solve any linear diffusion equation (homogeneous or non-homogeneous), with reasonable smoothness of the initial and boundary data.     

An iterative method based on equation decomposition for the fourth‐order singular perturbation problem

In this article, we propose an iterative method based on the equation decomposition technique ( 1 ) for the numerical solution of a singular perturbation problem of fourth‐order elliptic equation. At each step of the given method, we only need to solve a boundary value problem of second‐order elliptic equation and a second‐order singular perturbation problem. We prove that our approximate solution converges to the exact solution when the domain is a disc. Our numerical examples show the efficiency and accuracy of our method. Our iterative method works very well for singular perturbation problems, that is, the case of 0 < ε ? 1, and the convergence rate is very fast. © 2012 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013     

A Schwarz-based domain decomposition method for the dispersion equation

We propose a Schwarz-based domain decomposition method for solving a dispersion equation consisting on the linearized KdV equation without the advective term, using simple interface operators based on the exact transparent boundary conditions for this equation. An optimization process is performed for obtaining the approximation that provides the method with the fastest convergence to the solution of the monodomain problem.