用修正的平行割线法求解高阶奇异问题

许多实际问题中,方程在解点处的导算子为一高阶奇异算子,如反应扩散系统、优化问题中的歧点等.因此,对于求解高阶奇异问题的研究具有重要的实际意义.利用平行割线法求解高阶奇异问题,得到了渐近收敛速率,最后结合Hilbert空间几何特征,在几乎不增加计算量的前提下,修正了平行割线法,提高了渐近收敛速率.     

求解单变量无约束优化问题的一类新割线法

本文基于分式逼近提出了一类求解单变量无约束优化问题的新割线法,给出并证明了该方法的收敛阶是(√2+1).并进一步对新方法的性能进行了分析,给出了新方法、经典的牛顿法和其他修正的割线类方法解单变量无约束优化问题的数值实验.理论和数值结果均表明新的割线法是有效的.     

求解守恒型奇异摄动问题的一个一致收敛高阶方法

本文对守型恒奇异摄动问题(1.1)给出了一个一致收敛的高阶方法.首先将原问题(1.1)转换为二个一阶初值问题(1.4),即(1.1)的解是(1.4)的两个解的线性组合.然后对初值问题(1.4)构造了一个O(hm+1)一致收敛的差分格式.因此由关系式(1.3),我们得到了原问题的一个O(hm+1)一致精度的解,这里m是任意给定的非负整数.最后给出了数值结果.     

求解单调包含问题的惯性混合非精确邻近点算法

本文提出了求解单调包含问题的一类新的惯性混合非精确邻近点算法(简记为iHIPPA).在适当的参数假设下,我们证明了求解单调包含问题的iHIPPA所产生点列的弱收敛性,获得了iHIPPA的非渐近收敛率为(o)(1/√k)及iHIPPA的遍历迭代复杂性为(o)(1/√k).作为应用,我们还建立了求解单调变分包含问题的惯性邻...     

求解奇异摄动问题的一个耦合差分格式

本文考察奇异摄动问题(1.1).在一特殊的非均匀网格上,将不稳定、二阶精度的中心差格式和稳定、一阶精度的Abrahamsson-Keller-Kreiss箱子格式相耦合,得到了一个二阶一致收敛的差分格式.最后给出了数值结果.     

非线性互补问题非精确逐次逼近法的全局收敛性

本文针对非线性互补问题,提出了与其等价的非光滑方程的非精确逐次逼近算法,并在一定条件下证明了该算法的全局收敛性。     

求解奇异摄动转向点问题的高精度方法

1.引言 本文考察以下奇异摄动转向点问题: Lu≡ε~2u″+xa(x)u′-b(x)u=f(x),x∈I=[-1,1], u(-1)=A,u(1)=B, (1.1)其中参数ε是(0,1]中的常数,函数a(x)∈C~3[I],b(x),f(x)∈C~4[I]且满足a(x)≥a_*>0,b(x)≥b_*>0.在以上假设下,由[1]知,方程(1.1)存在唯一解u_8∈C~5[I]且     

基于修正割线方程的自适应BB法

基于一类带单参数γ的修正割线方程,给出了带参数γ的修正BB(BarzilaiBorwein)步长α_k(γ),并在某种意义下获得了γ的一个最优取值8/3.进而,依据当前和上一次迭代点连线段上目标函数的凸性,对步长α_k(γ)进行修正,并结合ZhangHager非单调线搜索技术,给出了求解无约束优化问题的一类自适应修正BB算法—AMBB算法.在适当的假设下,AMBB算法具有全局收敛性,且当目标函数为强凸函数时,AMBB算法具有线性收敛率.数值试验表明,给出的对应于参数γ取值8/3的AMBB算法是十分有效的.     

使用非单调技术的不精确预条件牛顿类方法解非线性方程组

本文提供了预条件不精确牛顿型方法结合非单调技术解光滑的非线性方程组．在合理的条件下证明了算法的整体收敛性．进一步，基于预条件收敛的性质，获得了算法的局部收敛速率，并指出如何选择势序列保证预条件不精确牛顿型的算法局部超线性收敛速率．     

New Inexact Line Search Method for Unconstrained Optimization

We propose a new inexact line search rule and analyze the global convergence and convergence rate of related descent methods. The new line search rule is similar to the Armijo line-search rule and contains it as a special case. We can choose a larger stepsize in each line-search procedure and maintain the global convergence of related line-search methods. This idea can make us design new line-search methods in some wider sense. In some special cases, the new descent method can reduce to the Barzilai and Borewein method. Numerical results show that the new line-search methods are efficient for solving unconstrained optimization problems. The work was supported by NSF of China Grant 10171054, Postdoctoral Fund of China, and K. C. Wong Postdoctoral Fund of CAS Grant 6765700. The authors thank the anonymous referees for constructive comments and suggestions that greatly improved the paper.     

奇异线性方程组的并行多分裂迭代法

改进了奇异M-矩阵的线性方程组的并行多分裂法的一些最近结果,给出了并行多分裂迭代方法的一些收敛性的理论结果。     

Inexact Interior-Point Method

In this paper, we introduce an inexact interior-point algorithm for a constrained system of equations. The formulation of the problem is quite general and includes nonlinear complementarity problems of various kinds. In our convergence theory, we interpret the inexact interior-point method as an inexact Newton method. This enables us to establish a global convergence theory for the proposed algorithm. Under the additional assumption of the invertibility of the Jacobian at the solution, the superlinear convergence of the iteration sequence is proved.     

Inexact GMRES for singular linear systems

Inexact Krylov subspace methods have been shown to be practical alternatives for the solution of certain linear systems of equations. In this paper, the solution of singular systems with inexact matrix-vector products is explored. Criteria are developed to prescribe how inexact the matrix-vector products can be, so that the computed residual remains close to the true residual, thus making the inexact method of practical applicability. Cases are identified for which the methods work well, and this is the case in particular for systems representing certain Markov chains. Numerical experiments illustrate the effectiveness of the inexact approach. AMS subject classification (2000)  65F10     

A Structured Secant Method Based on a New Quasi-Newton Equation for Nonlinear Least Squares Problems

In this paper, a new quasi-Newton equation is applied to the structured secant methods for nonlinear least squares problems. We show that the new equation is better than the original quasi-Newton equation as it provides a more accurate approximation to the second order information. Furthermore, combining the new quasi-Newton equation with a product structure, a new algorithm is established. It is shown that the resulting algorithm is quadratically convergent for the zero-residual case and superlinearly convergent for the nonzero-residual case. In order to compare the new algorithm with some related methods, our preliminary numerical experiments are also reported.     

Globally Convergent Inexact Generalized Newton Method for First-Order Differentiable Optimization Problems

Motivated by the method of Martinez and Qi (Ref. 1), we propose in this paper a globally convergent inexact generalized Newton method to solve unconstrained optimization problems in which the objective functions have Lipschitz continuous gradient functions, but are not twice differentiable. This method is implementable, globally convergent, and produces monotonically decreasing function values. We prove that the method has locally superlinear convergence or even quadratic convergence rate under some mild conditions, which do not assume the convexity of the functions.     

New Inexact Parallel Variable Distribution Algorithms

We consider the recently proposed parallel variable distribution(PVD) algorithm of Ferris and Mangasarian [4] for solvingoptimization problems in which the variables are distributed amongp processors. Each processor has the primary responsibility forupdating its block of variables while allowing the remainingsecondary variables tochange in a restricted fashion along some easily computable directions.We propose useful generalizationsthat consist, for the general unconstrained case, of replacing exact global solution ofthe subproblems by a certain natural sufficient descent condition, and,for the convex case, of inexact subproblem solution in thePVD algorithm. These modifications are the key features ofthe algorithm that has not been analyzed before.The proposed modified algorithms are more practical andmake it easier to achieve good load balancing among the parallelprocessors.We present a general framework for the analysis of thisclass of algorithms and derive some new and improved linear convergence resultsfor problems with weak sharp minima of order 2 and strongly convex problems.We also show that nonmonotone synchronization schemesare admissible, which further improves flexibility of PVD approach.     

Convergence Analysis of an Inexact Infeasible Interior Point Method for Semidefinite Programming

In this paper we present an extension to SDP of the well known infeasible Interior Point method for linear programming of Kojima, Megiddo and Mizuno (A primal-dual infeasible-interior-point algorithm for Linear Programming, Math. Progr., 1993). The extension developed here allows the use of inexact search directions; i.e., the linear systems defining the search directions can be solved with an accuracy that increases as the solution is approached. A convergence analysis is carried out and the global convergence of the method is proved.     

一种双割线折线法求解信赖域子问题

结合利用Hessian阵的特征值性质,本文提出求解信赖域子问题的一种双割线折线法,它不同于Powell的单折线,Dennis的双折线和赵英良的切线单折线.在适当条件下,分析双割线折线路径的性质,且证明了算法的收敛性.数值试验表明,这种新算法是有效且可行的.