一类非精确线性搜索共轭梯度新算法

本文通过对迭代参数的适当选取，给出了一类共轭梯度新算法。在算法的迭代过程中，迭代方向保持下降性，在一般的非精确线性搜索条件下，算法的全局收敛性得到了证明。     

无约束最优化的微分下降法

本文利用曲线线性搜索法和最优化的微分梯度法的特点,提出了一种一般的曲线搜索方式:微分下降法。这种方法通过下降方向对确定迭代矩阵,由初值微分方程的解析解确定迭代搜索曲线。本文给出了算法的整体收敛性证明,并给出了满意的数值实验结果。     

一类带线搜索的非单调信赖域新算法

结合非单调信赖域方法,和非单调线搜索技术,提出了一种新的无约束优化算法.信赖域方法的每一步采用线搜索,使得迭代每一步都充分下降加快了迭代速度.在一定条件下,证明了算法具有全局收敛性和局部超线性.收敛速度.数值试验表明算法是十分有效的.     

一类新共轭下降算法的全局收敛性

本文提出一类新的共轭下降算法,在算法的迭代过程中,迭代方向保持下降性,在非精确线搜索下,证明了算法的整体收敛性.     

Armijo搜索下的记忆梯度法及其收敛性

本文提出一种新的无约束优化记忆梯度算法,在Armijo搜索下,该算法在每步迭代时利用了前面迭代点的信息,增加了参数选择的自由度,适于求解大规模无约束优化问题。分析了算法的全局收敛性。     

一类新的曲线搜索下的多步下降算法

提出一类新的曲线搜索下的多步下降算法,在较弱条件下证明了算法具有全局收敛性和线性收敛速率.算法利用前面多步迭代点的信息和曲线搜索技巧产生新的迭代点,收敛稳定,不用计算和存储矩阵,适于求解大规模优化问题.数值试验表明算法是有效的.     

不可微合成函数的极小化方法

本文提出了一种极小化不可微合成函数的下降算法。该算法通过内部迭代寻找下降方向，每次内部迭代求解一个二次规划。外部迭代点不精确线搜索求得，算法在有限步内得到近似平稳点，经过适当修正后，算法全局收敛到平衡点。     

解无约束优化问题的一个新的带线搜索的信赖域算法

在传统信赖域方法的基础上, 提出了求解无约束最优化问题的一个新的带线搜索的信赖域算法. 该算法采用大步长 Armijo 线搜索技术获得迭代步长, 克服了每次迭代求解信赖域子问题时计算量较大的缺点, 因而适用于求解大型的优化问题. 在适当的条件下, 我们证明了算法的全局收敛性. 数值实验结果表明本文所提出的算法是有效的.     

一类广义拟牛顿算法的收敛性

本文提出一类广义拟牛顿算法,新类算法降低了关于目标函数的假设条件,将线搜索扩展 到一般形式,它概括了若干种常用的非精确线搜索技术.此外,算法对迭代校正公式中的参数Φk的 选取范围做了较大扩展(可以取负值).     

一类具约束选址模型的组合算法

针对一般具闭凸集约束的单址选址模型,提出具全局收敛性的组合算法．算法在迭代中先采用信赖域技巧,当出现“内循环”时,则改用不做线搜索的梯度法．该算法既具有信赖域算法的优越性,又避免了出现“内循环”时速成的隐迭代．同时,该算法通常不需进行线搜索,较之其它组合算法更加简捷实用．     

A PROCESS FOR SOLVING A FEW EXTREME EIGENPAIRS OF LARGE SPARSE POSITIVE DEFINITE GENERALIZED EIGENVALUE PROBLEM

1. IntroductionWe are concerned in this work with finding a few extreme eigenvalues and theircorresponding eigenvectors of a generalized large scale eigenvalue problem in which thematrices are sparse and symmetric positive definite.Although finding a few extreme eigenpairs is of interest both in theory and practice,there are only few usable and efficient methods up to now. Reinsch and Baner ([12]),suggested a oR algorithm with Newton shift for the standard eigenproblem which included an ingen…     

A Two-Phase Algorithm for a Variational Inequality Formulation of Equilibrium Problems

We introduce an explicit algorithm for solving nonsmooth equilibrium problems in finite-dimensional spaces. A particular iteration proceeds in two phases. In the first phase, an orthogonal projection onto the feasible set is replaced by projections onto suitable hyperplanes. In the second phase, a projected subgradient type iteration is replaced by a specific projection onto a halfspace. We prove, under suitable assumptions, convergence of the whole generated sequence to a solution of the problem. The proposed algorithm has a low computational cost per iteration and, some numerical results are reported.     

A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization

By using the Moreau-Yosida regularization and proximal method, a new trust region algorithm is proposed for nonsmooth convex minimization. A cubic subproblem with adaptive parameter is solved at each iteration. The global convergence and Q-superlinear convergence are established under some suitable conditions. The overall iteration bound of the proposed algorithm is discussed. Preliminary numerical experience is reported.     

等式与界约束非线性优化的信赖域增广Lagrangian算法

１．引 言本文讨论如下非线性约束优化问题：其中； 是Ｒｎ→Ｒ的可微函数，　　　　　　．记 问题（１．１）是非线性约束优化问题中的一类重要类型，事实上任一个非线性等式与不等式约束优化均可引入松驰变量转化为（１．１）的形式．因此（１．１）的求解是人们讨论的热点问     

关于有限族Lipschitz映射多步Ishikawa迭代算法的收敛性及其应用

本文的目的是研究Lipschitz映射公共不动点问题.基于传统的Ishikawa迭代和Noor迭代方法,我们引入多步Ishikawa迭代算法,并且分别给出了该算法强收敛于有限族拟-Lipschitz映射和伪压缩映射公共不动点的充分必要条件.此外,我们证明了该算法强收敛到非扩张映射的公共不动点.作为应用,我们给出数值试验证实所得的结论.     

一类带非单调线搜索的信赖域算法

通过将非单调Wolfe线搜索技术与传统的信赖域算法相结合,我们提出了一类新的求解无约束最优化问题的信赖域算法.新算法在每一迭代步只需求解一次信赖域子问题,而且在每一迭代步Hesse阵的近似都满足拟牛顿条件并保持正定传递.在一定条件下,证明了算法的全局收敛性和强收敛性.数值试验表明新算法继承了非单调技术的优点,对于求解某...     

The Algorithm of Sequential KKT Equations by Nonmonotone Search for Arbitrary Initial Point

In this paper, a new algorithm for arbitrary initial point is proposed. The feature of the algorithm is that only KKT equations are solved in each iteration which greatly decreases the amount of computation. Under some proper assumptions, the algorithm is globally and superlinearly convergent.     

非线性约束条件下的SQP可行方法

本文对非线性规划问题给出了一个具有一步超线性收敛速度的可行方法。由于此算法每步迭代均在可行域内进行，并且每步迭代只需计算一个二次子规划和一个逆矩阵，因而算法具有较好的实用价值。本文还在较弱的条件下证明了算法的全局收敛和一步超线性收敛性。     

Iterative methods for the computation of fixed points of demicontractive mappings

This paper surveys some of the main convergence properties of the Mann-type iteration for the demicontractive mappings. Some variants of the Mann iteration that ensure the strong convergence, like the (CQ) algorithm and a variant for the asymptotically demicontractive mappings are also considered. The usual framework of our study is a (real) Hilbert space and only to a certain extent some particular Banach spaces. Historical aspects are pointed out and some applications for the convex feasibility problem are discussed.     

Smoothing SQP algorithm for semismooth equations with box constraints

In this paper, in order to solve semismooth equations with box constraints, we present a class of smoothing SQP algorithms using the regularized-smooth techniques. The main difference of our algorithm from some related literature is that the correspondent objective function arising from the equation system is not required to be continuously differentiable. Under the appropriate conditions, we prove the global convergence theorem, in other words, any accumulation point of the iteration point sequence generated by the proposed algorithm is a KKT point of the corresponding optimization problem with box constraints. Particularly, if an accumulation point of the iteration sequence is a vertex of box constraints and additionally, its corresponding KKT multipliers satisfy strictly complementary conditions, the gradient projection of the iteration sequence finitely terminates at this vertex. Furthermore, under local error bound conditions which are weaker than BD-regular conditions, we show that the proposed algorithm converges superlinearly. Finally, the promising numerical results demonstrate that the proposed smoothing SQP algorithm is an effective method.