拟变分不等式研究及其在交通问题中的应用

本文研究一类拟变分不等式问题解的存在性与唯一性,构造解的两种算法,即投影算法与超平面投影算法,给出相应的收敛性分析.作为本文结果的应用,我们研究了交通流量和环境影响的问题,并通过数值实验模拟相关结果.     

广义投影梯度型约束变尺度法

本文将广义投影梯度方向移植到约束变尺度算法之中，得到了一类新型算法：广义投影梯度型约束变尺度算法，并成功的使用了Ａｒｍｉｊｏ规则．该算法将广义投影类可行方向法和约束变尺度算法的优点溶为一体．并且由于使用了拟下降的概念，算法变得更为灵活．     

一般线性或非线性约束下的共轭投影变尺度方法

梯度投影法是一类有效的约束最优化算法，在最优化领域中占有重要的地位．但是，梯度投影法所采用的投影是正交投影，不包含目标函数和约束函数的二阶导数信息·因而；收敛速度不太令人满意．本文介绍一种共轭投影概念，利用共轭投影构造了一般线性或非线性约束下的共轭投影变尺度算法，并证明了算法在一定条件下具有全局收敛性．由于算法中的共轭投影恰当地包含了目标函数和约束函数的二阶导数信息，因而收敛速度有希望加快．数值试验的结果表明算法是有效的．     

ABS投影算法的两个改进

本文将求解线性方程的ABS投影算法进行两方面的改进和推广，一是使算法在第K次迭代产生的点xk＋1不仅满足前k个方程，还尽可能地使得在点xk处成立的方程j（j＞k）在xk＋1处仍成立，称之为强ABS投影算法，另外初始选代矩阵由非奇异的减弱为任意的．二是建立了系数矩阵有零子块的方程组的ABS投影算法，其存贮量和计算量比原ABS投影算法小．ABS算法可以作为这两种改进算法的特别情形．     

初始点任意的一个非线性优化的广义梯度投影法

广义投影算法的优点是避免转轴运算。它成功地给出了线性约束问题、初始点任意的只带非线性不等式约束问题，以及利用辅助规划来处理带等式与不等式约束问题的算法．后者完满地解决了投影算法对于非线性等式约束问题的处理，但要求满足不等式约束的初始点．本文据此利用广义投影与罚函数技巧给出了一个初始点任意的等式与不等式约束问题的算法，省去了求初始解的计算，并保持了上述方法的优点，证明了算法的全局收敛性     

一般凸规划的次梯度投影算法

一般凸规划的次梯度投影算法夏建业（广州金融专科学校基础部）本文对一般非光滑约束凸规划给出了一个新的可行方向算法，此算法是通过修改和推广Ｖ．ＰＳｒｅｅｄ－ｈａｒａｎ［８，９］对某一特定类型非光滑约束凸规划提出的次梯度投影法所得到的．对此算法，本文讨论了...     

怎样识别和避开数学试题中的“陷阱”

命题者针对学生知识和思维方面的“漏洞”，常在试题中巧妙而隐蔽蜮设置一些“陷阱”，以考查学生思维的敏捷性和产密性，区分学生的思维能力．那么怎样才能准确地识别“陷阱”，并迅速地避开“陷阱”，实现正确解题呢？     

解非线性单调方程组的投影自调比对称秩1拟牛顿法

本文给出了求解非线性单调方程组的两个自调比对称秩1牛顿法,即投影SSR1法和投影有限储存SSR1法.这两个算法将自调比对称秩1校正参数进行了一个简单的修改并采用了保守策略.在非线性单调函数满足李普希茨连续的条件下,证明了算法的全局收敛性,并与相同类型的BFGS法进行了初步的数值比较试验,试验结果表明自调比对称秩1类投影算法求解非线性单调方程组与相同类型的BFGS数值结果相当.     

一族超线性收敛的投影拟牛顿算法

本文将梯度投影与拟牛顿法相结合，给出了求解一般线性约束非线性规划问题含两组参数的算法族．在一定的条件下证明了算法族的全局收敛性与它的子族的超线性收敛速度，并给出了投影Ｄ．Ｆ．Ｐ方法、投影ＢＦＧＳ方法等一些特例．     

带1-范数约束的分裂可行问题的投影算法

本文主要研究带1-范数约束的分裂可行问题的求解算法.用一种交替投影算法,求得了问题的解,提出松弛交替投影算法,改进了直接往闭凸集上投影这一不足,并证明了该算法的收敛性.     

Analysis on Newton projection method for the split feasibility problem

In this paper, based on a merit function of the split feasibility problem (SFP), we present a Newton projection method for solving it and analyze the convergence properties of the method. The merit function is differentiable and convex. But its gradient is a linear composite function of the projection operator, so it is nonsmooth in general. We prove that the sequence of iterates converges globally to a solution of the SFP as long as the regularization parameter matrix in the algorithm is chosen properly. Especially, under some local assumptions which are necessary for the case where the projection operator is nonsmooth, we prove that the sequence of iterates generated by the algorithm superlinearly converges to a regular solution of the SFP. Finally, some numerical results are presented.     

A reflection on the implicitly restarted Arnoldi method for computing eigenvalues near a vertical line

In this article, we will study the link between a method for computing eigenvalues closest to the imaginary axis and the implicitly restarted Arnoldi method. The extension to computing eigenvalues closest to a vertical line is straightforward, by incorporating a shift. Without loss of generality we will restrict ourselves here to computing eigenvalues closest to the imaginary axis.In a recent publication, Meerbergen and Spence discussed a new approach for detecting purely imaginary eigenvalues corresponding to Hopf bifurcations, which is of interest for the stability of dynamical systems. The novel method is based on inverse iteration (inverse power method) applied on a Lyapunov-like eigenvalue problem. To reduce the computational overhead significantly a projection was added.This method can also be used for computing eigenvalues of a matrix pencil near a vertical line in the complex plane. We will prove in this paper that the combination of inverse iteration with the projection step is equivalent to Sorensen’s implicitly restarted Arnoldi method utilizing well-chosen shifts.     

A Finite Element Algorithm for Nematic Liquid Crystal Flow Based on the Gauge-Uzawa Method

In this paper,we present a finite element algorithm for the time-dependent nematic liquid crystal flow based on the Gauge-Uzawa method.This algorithm combines the Gauge and Uzawa methods within a finite element variational formulation,which is a fully discrete projection type algorithm,whereas many projection methods have been studied without space discretization.Besides,error estimates for velocity and molecular orientation of the nematic liquid crystal flow are shown.Finally,numerical results are given to show that the presented algorithm is reliable and confirm the theoretical analysis.     

Convergence theorems based on the shrinking projection method for variational inequality and?equilibrium problems

The purpose of this paper is to introduce a hybrid projection algorithm based on the shrinking projection method for two relatively weak nonexpansive mappings. We prove strong convergence theorem which approximate the common element in the fixed point set of two such mappings, the solution set of the variational inequality and the solution set of the equilibrium problem in the framework of Banach spaces. Our results improve and extend previous results.     

Accelerated reflection projection algorithm and its application to the LMI problem

We discuss accelerated version of the alternating projection method which can be applied to solve the linear matrix inequality (LMI) problem. The alternating projection method is a well-known algorithm for the convex feasibility problem, and has many generalizations and extensions. Bauschke and Kruk proposed a reflection projection algorithm for computing a point in the intersection of an obtuse cone and a closed convex set. We carry on this research in two directions. First, we present an accelerated version of the reflection projection algorithm, and prove its weak convergence in a Hilbert space; second, we prove the finite termination of an algorithm which is based on the proposed algorithm and provide an explicit upper bound for the required number of iterations under certain assumptions. Numerical experiments for the LMI problem are provided to demonstrate the effectiveness and merits of the proposed algorithms.     

A feasible direction method for the semidefinite program with box constraints

In this paper, we try to solve the semidefinite program with box constraints. Since the traditional projection method for constrained optimization with box constraints is not suitable to the semidefinite constraints, we present a new algorithm based on the feasible direction method. In the paper, we discuss two cases: the objective function in semidefinite programming is linear and nonlinear, respectively. We establish the convergence of our algorithm, and report the numerical experiments which show the effectiveness of the algorithm.     

A NEW SECANT METHOD FOR NONLINEAR LEAST SQUARES PROBLEMS

This paper is concerned with the solution of the nonlinear least squares problems. A new secant method is suggested in this paper, which is based on an affine model of the objective function and updates the first-order approximation each step when the iterations proceed. We present an algorithm which combines the new secant method with Gauss-Newton method for general nonlinear least squares problems. Furthermore, we prove that this algorithm is Q-superlinearly convergent for large residual problems under mild conditions.     

A projection method to solve linear systems in tensor format

In this paper, we propose a method for the numerical solution of linear systems of equations in low rank tensor format. Such systems may arise from the discretisation of PDEs in high dimensions, but our method is not limited to this type of application. We present an iterative scheme, which is based on the projection of the residual to a low dimensional subspace. The subspace is spanned by vectors in low rank tensor format which—similarly to Krylov subspace methods—stem from the subsequent (approximate) application of the given matrix to the residual. All calculations are performed in hierarchical Tucker format, which allows for applications in high dimensions. The mode size dependency is treated by a multilevel method. We present numerical examples that include high‐dimensional convection–diffusion equations.Copyright © 2012 John Wiley & Sons, Ltd.     

Spectral behaviour of GMRES applied to singular systems

The purpose of this paper is to develop a spectral analysis of the Hessenberg matrix obtained by the GMRES algorithm used for solving a linear system with a singular matrix. We prove that the singularity of the Hessenberg matrix depends on the nature of A and some other criteria such as the zero eigenvalue multiplicity and the projection of the initial residual on particular subspaces. We also show some new results about the distinct kinds of breakdown which may occur in the algorithm when the system is singular.