关于非线性规划的同伦算法与罚函数法

本文揭示了关于非线性规划问题的同伦算法与外点罚函数法的关系，并讨论了有关同伦算法的收敛条件，给出了一些典型的检验问题的计算结果以表明利用结构的分段线性同伦算法的有效性。     

一类非凸区域的拟法锥构造方法及其在非凸规划求解中的应用

本文给出基于球形的一类满足拟法锥条件区域的拟法锥构造方法,基于该可行域的拟法锥,建立求解在该类非凸区域上的规划问题的K-K-T点的部分凝聚同伦组合方程,并证明了该同伦内点法的整体收敛性,给出实现同伦内点法的具体数值跟踪算法步骤,并通过数值例子证明算法是可行的和有效的．     

相对同伦满与相对同伦单的局部化

设ｆ：ＸＹ是相对同伦满或相对同伦单．本文考虑在什么条件下，它的ｐ局部化ｆｐ：ＸｐＹｐ也是相对同伦满或相对同伦单．ｐ是素数或零     

线性l1模极小化问题的熵函数延拓法

本文通过利用极大熵函数构造同伦映射，建立了求解无约束线性l1模问题的熵函数延拓算法，证明了方法的收敛性，并给出了数值算例．     

关于同伦满态与覆叠空间

本文在点标道路连通ＣＷ空间的同伦范畴中，利用同伦推出示性了同伦满态，得出了若ｆ：Ｘ－Ｙ是同伦满态，则对π１Ｙ的任一正规子群Ｈ，升腾映射ｆ：Ｘ（ｆ－１＃（Ｈ））→■（Ｈ）也是同伦满态．     

弱拟法锥条件下非凸优化问题的同伦算法

本文给出弱拟法锥条件的定义,并针对非线性组合同伦方程,得到在弱拟法锥条件下求解约束非凸优化问题的同伦内点算法.证明了该算法对于可行域的某个子集中几乎所有的点,同伦路径存在,并且同伦路径收敛于问题的K-K-T点,通过数值例子验证了该算法是有效的.     

大范围求解非线性方程组的指数同伦法

为了解决关于奇异的非线性方程组求根问题,提出了一种由同伦算法推出大范围收敛的连续型方法-指数同伦法,构造了一类指数同伦方程,克服了Jacobi矩阵的奇异,分析了指数同伦方     

关于二次规划问题分段线性同伦算法的改进

本文利用Ｃｈｏｌｅｓｋｙ分解，Ｇａｕｓｓ消去等技术和定义适当的同伦映射，将关于二次规划问题的分段线性同伦算法加以改进，改进后的算法，对于严格凸二次规划来说，计算效率与Ｇｏｌｄｆａｒｂ－Ｉｄｎａｎｉ的对偶法相当。     

同伦方法求解无界域上非凸规划问题的收敛性定理

利用同伦方法求解非凸规划时,一般只能得到问题的K-K-T点.本文得到无界域上同伦方法求解非凸规划的几个收敛性定理,证明在一定条件下,通过构造合适的同伦方程,同伦算法收敛到问题的局部最优解.     

单参数非线性问题中高阶奇异点的计算

本文考虑计算单参数非线性问题中高阶奇异点的数值方法，基于确定奇异点的一个普适的扩张系统，结合同伦参数的拟弧长延拓，给出了计算各类高阶奇异点的一个统一算法，数值例子表明了算法的有效性．     

求解极小极大问题的非单调过滤算法

提出一种改进的求解极小极大问题的信赖域滤子方法,利用SQP子问题来求一个试探步,尾服用滤子来衡量是否接受试探步,避免了罚函数的使用;并且借用已有文献的思想, 使用了Lagrange函数作为效益函数和非单调技术,在适当的条件下,分析了算法的全局和局部收敛性,并进行了数值实验.     

Stochastic Comparison Algorithm for Discrete Optimization with Estimation of Time-Varying Objective Functions

In this paper, the optimization of time-varying objective functions, known only through estimates, is considered. Recent research defined algorithms for static optimization problems. Based on one of these algorithms, we derive an optimization scheme for the time-varying case. In stochastic optimization problems, convergence of an algorithm to the optimum prevents the algorithm from being efficiently adaptive to changes of the objective function if it is time-varying. So, convergence cannot be required in a time-varying scenario. Rather, we require convergence to the optimum with high probability together with a satisfactory dynamical behavior. Analytical and simulative results illustrate the performance of the proposed algorithm compared with other optimization techniques.     

Optimization algorithm with probabilistic estimation

In this paper, we present a stochastic optimization algorithm based on the idea of the gradient method which incorporates a new adaptive-precision technique. Because of this new technique, unlike recent methods, the proposed algorithm adaptively selects the precision without any need for prior knowledge on the speed of convergence of the generated sequence. With this new technique, the algorithm can avoid increasing the estimation precision unnecessarily, yet it retains its favorable convergence properties. In fact, it tries to maintain a nice balance between the requirements for computational accuracy and those for computational expediency. Furthermore, we present two types of convergence results delineating under what assumptions what kinds of convergence can be obtained for the proposed algorithm.The work reported here was supported in part by NSF Grant No. ECS-85-06249 and USAF Grant No. AFOSR-89-0518. The authors wish to thank the anonymous reviewers whose careful reading and criticism have helped them improve the paper considerably.     

Generalized Newton’s method based on graphical derivatives

This paper concerns developing a numerical method of the Newton type to solve systems of nonlinear equations described by nonsmooth continuous functions. We propose and justify a new generalized Newton algorithm based on graphical derivatives, which have never been used to derive a Newton-type method for solving nonsmooth equations. Based on advanced techniques of variational analysis and generalized differentiation, we establish the well-posedness of the algorithm, its local superlinear convergence, and its global convergence of the Kantorovich type. Our convergence results hold with no semismoothness and Lipschitzian assumptions, which is illustrated by examples. The algorithm and main results obtained in the paper are compared with well-recognized semismooth and B-differentiable versions of Newton’s method for nonsmooth Lipschitzian equations.     

A modified fixed-point iterative algorithm for image restoration using fourth-order PDE model

In this paper, we propose a modified fixed point iterative algorithm to solve the fourth-order PDE model for image restoration problem. Compared with the standard fixed point algorithm, the proposed algorithm needn?t to compute inverse matrices so that it can speed up the convergence and reduce the roundoff error. Furthermore, we prove the convergence of the proposed algorithm and give some experimental results to illustrate its effectiveness by comparing with the standard fixed point algorithm, the time marching algorithm and the split Bregman algorithm.     

A generalized contraction proximal point algorithm with two monotone operators

Abstract

In this work, we introduce a generalized contraction proximal point algorithm and use it to approximate common zeros of maximal monotone operators A and B in a real Hilbert space setting. The algorithm is a two step procedure that alternates the resolvents of these operators and uses general assumptions on the parameters involved. For particular cases, these relaxed parameters improve the convergence rate of the algorithm. A strong convergence result associated with the algorithm is proved under mild conditions on the parameters. Our main result improves and extends several results in the literature.     

A Rapidly Convergence Algorithm for Linear Search and its Application

The essence of the linear search is one-dimension nonlinear minimization problem, which is an important part of the multi-nonlinear optimization, it will be spend the most of operation count for solving optimization problem. To improve the efficiency, we set about from quadratic interpolation, combine the advantage of the quadratic convergence rate of Newton's method and adopt the idea of Anderson-Bjorck extrapolation, then we present a rapidly convergence algorithm and give its corresponding convergence conclusions. Finally we did the numerical experiments with the some well-known test functions for optimization and the application test of the ANN learning examples. The experiment results showed the validity of the algorithm.     

An algebraic multigrid method for high order time-discretizations of the div-grad and the curl-curl equations

We present an algebraic multigrid algorithm for fully coupled implicit Runge–Kutta and Boundary Value Method time-discretizations of the div-grad and curl-curl equations. The algorithm uses a blocksmoother and a multigrid hierarchy derived from the hierarchy built by any algebraic multigrid algorithm for the stationary version of the problem. By a theoretical analysis and numerical experiments, we show that the convergence is similar to or better than the convergence of the scalar algebraic multigrid algorithm on which it is based. The algorithm benefits from several possibilities for implementation optimization. This results in a computational complexity which, for a modest number of stages, scales almost linearly as a function of the number of variables.     

求解带线性约束的凸优化的一类自适应不定线性化增广拉格朗日方法

增广拉格朗日方法是求解带线性约束的凸优化问题的有效算法.线性化增广拉格朗日方法通过线性化增广拉格朗日函数的二次罚项并加上一个临近正则项,使得子问题容易求解,其中正则项系数的恰当选取对算法的收敛性和收敛速度至关重要.较大的系数可保证算法收敛性,但容易导致小步长.较小的系数允许迭代步长增大,但容易导致算法不收敛.本文考虑求解带线性等式或不等式约束的凸优化问题.我们利用自适应技术设计了一类不定线性化增广拉格朗日方法,即利用当前迭代点的信息自适应选取合适的正则项系数,在保证收敛性的前提下尽量使得子问题步长选择范围更大,从而提高算法收敛速度.我们从理论上证明了算法的全局收敛性,并利用数值实验说明了算法的有效性.     

Parareal算法的均方稳定性分析

Parareal算法是一种非常有效的实时并行计算方法.与传统的并行计算方法相比,该算法的显著特点是它的时间并行性-先将整个计算时间划分成若干个子区间,然后在每个子区间内同时进行计算.Parareal算法收敛速度快,并行效率高,且易于编程实现,从2001年由Lions,Maday和Turinici等人首次提出至今,在短短...