P*(κ)阵线性互补问题一种新的宽邻域预估-校正内点算法

基于邻近度量函数的最小值,对P*(κ)阵线性互补问题提出了一种新的宽邻域预估-校正算法,在较一般的条件下,证明了算法的迭代复杂性为O(κ+1)23n log(x0ε)Ts0.算法既可视为Miao的P*(κ)阵线性互补问题Mizuno-Todd-Ye预估-校正内点算法的一种变形,也可以视为最近Zhao提出的线性规划基于邻近度量函数最小值的宽邻域内点算法的推广.     

A Mehrotra-type predictor-corrector algorithm with polynomiality andQ-subquadratic convergence

Mehrotra's predictor-corrector algorithm [3] is currently considered to be one of the most practically efficient interior-point methods for linear programming. Recently, Zhang and Zhang [18] studied the global convergence properties of the Mehrotra-type predictor-corrector approach and established polynomial complexity bounds for two interior-point algorithms that use the Mehrotra predictor-corrector approach. In this paper, we study the asymptotic convergence rate for the Mehrotra-type predictor-corrector interior-point algorithms. In particular, we construct an infeasible-interior-point algorithm based on the second algorithm proposed in [18] and show that while retaining a complexity bound ofO(n ^1.5 t)-iterations, under certain conditions the algorithm also possesses aQ-subquadratic convergence, i.e., a convergence ofQ-order 2 with an unboundedQ-factor.Research supported in part by NSF DMS-9102761 and DOE DE-FG02-93ER25171.     

高斯光束通过克尔介质的光强分布研究

以亚当姆预测 校正系统数值计算方法为基础,在同时考虑光束通过克尔介质后的相位和光束线型变化的情况下,利用惠更斯 菲涅尔衍射积分公式,对高斯光束通过克尔介质后的近场和远场的横向光强分布进行了研究.结果表明：当高斯光束通过“薄”介质的出射面时,不仅仅相位发生了变化,而且光强线型分布也不再呈理想的高斯分布,而是有一系列丝状的近高斯分布.光束通过克尔介质后,无论是在近场还是远场,其光束总能量分布都被发散了.结果同时表明：光束通过正克尔介质后的远场光强分布主要集中在中心附近,并逐渐向边缘减弱,而光束通过负克尔介质后远场光束能量主要集中在边缘.     

变形双重介质分形油藏非达西流动分析

考虑与实际生产相符的介质的双重特性和分形特征,并考虑介质的变形,引入双重分形介质渗透率模数,建立应力敏感地层双重分形介质系统的流动方程.采用Douglas-Jones预估-校正法获得无限大地层定产量生产时变形双重分形介质模型的数值解和无限大地层定压生产时分形介质双孔模型的数值解,作出了典型的压力曲线图版.     

Predictor-Corrector Smoothing Methods for Monotone LCP

In this paper, we analyze the global and local convergence properties of two predictor-corrector smoothing methods, which are based on the framework of the method in [1], for monotone linear complementarity problems (LCPs). The difference between the algorithm in [1] and our algorithms is that the neighborhood of smoothing central path in our paper is different to that in [1]. In addition, the difference between Algorithm 2.1 and the algorithm in [1] exists in the calculation of the predictor step. Comparing with the results in [1],the global and local convergence of the two methods can be obtained under very mild conditions. The global convergence of the two methods do not need the boundness of the inverse of the Jacobian. The superlinear convergence of Algorithm 2.1‘ is obtained under the assumption of nonsingularity of generalized Jacobian of Φ(x,y) at the limit point and Algorithm 2.1 obtains superlinear convergence under the assumption of strict complementarity at the solution. The efficiency of the two methods is tested by numerical experiments.     

一类凸规划的多项式预估校正内点法

1、引言 1990年由Mehrotra对线性规划问题提出了一个称为预估校正的方法,并在1992年给出了其数值算法．1993年Mizuno,Todd和Y．Ye．给出了改进的预估校正内点法,使得一个预估步后只跟一个校正步．1994年F．A．Potra给出了不可行预估校正内点法,使得可以从一个不可行的初始点开始算法的迭代,并证明了其为二次收敛．     

基于预估-校正的Generalized-α法及其在非线性结构动力学中的应用

直接积分法是求解动力学方程的一种有效方法。应用一种预估-校正的Generalized-α法对结构大变形动力学问题进行分析求解,并与Newmark法和Bathe法进行对比研究。首先预估当前计算步的解,然后以预估值作为起始值进行非线性迭代计算,并对解不断校正,直到满足收敛条件,进入下一时间步的计算。在保证Generalized-α法性能的基础上,简化了非线性迭代公式,便于编程实现。通过壳和实体的大变形动力学算例,证明了本文方法具有较高的稳定性和精度。     

Infeasible Predictor-Corrector Interior-Point Method Applied to Image Restoration in the Presence of Noise

Image recovery problems can be solved using optimization techniques. They lead often to the solution of either a large-scale convex quadratic program or equivalently a nondifferentiable minimization problem. To solve the quadratic program, we use an infeasible predictor-corrector interior-point method, presented in the more general framework of monotone LCP. The algorithm has polynomial complexity and it converges with asymptotic quadratic rate. When implementing the method to recover images, we take advantage of the underlying sparsity of the problem. We obtain good performances, that we assess by comparing the method with a variable-metric proximal bundle algorithm applied to the solution of equivalent nonsmooth problem.     

P*(k)线性互补问题的Mehrotra型预估-校正算法复杂性分析

本文提出一种求解单调非线性互补问题的Mehrotra型预估-校正算法.新算法采用不同的自适应更新策略.在尺度化的Lipschitz条件下,证明了新算法的迭代复杂性为O(n2 log (x0)T s0/ε)),其中(x0,s0)为初始点,ε为精度.