条件收敛的

FR conjugate gradient methods with perturbations are proposed. The global convergence property of the first method is proved under the condition of main directions' sufficient descent. Whereas, in the proof of the convergence for the other two methods, we only need main directions' descent. Importantly and quite interesting, boundedness conditions such as objective function being bounded below, boundedness of level set are not needed. Chapter 5 presents a version of Dai-Yuan conjugate gradient method with perturbations.

在主方向充分下降的条件下证明了第一个方法的全局收敛性，而后两个方法的收敛性是在主方向下降的条件下证明的，这些收敛性证明的一个共同特征就是不需要目标函数有下界或水平集有界等有界性条件，第5章采用Wolfe或Armijo步长规则提出了带扰动项的Dai-Yuanabbr。

In 1947, Hus and Robbin defined the complete convergence, which is stronger than almost everywhere convergence.

Hus和Robbin于1947年提出了完全收敛性的概念，完全收敛比几乎处处收敛条件更严格。

In Chapter 3,we discuss theconvergence of Newton's method for nonlinear inclusion problems with values in aclosed convex cone,using the new theory developed by Wang Xinghua,we generalizeRobinson's Kantorovich theorem for this problem to a more widely function classes,inthe same time our results take Smale theorem as a special case.

在第三章，我们利用王兴华最近提出的理论框架研究在闭凸锥中取值的非线性包含问题的Newton方法，将Robinson在特殊Lipshitz条件下建立的Kantorovich型收敛性定理推广至满足中心Lipshitz条件的函数类，我们建立的一般的收敛性定理同时将Smale型收敛性定理包容其中。

In conclusion that there is one and only one balance convergence point is proved and convergence error equation is given when convergence condition is satisfied.

对算法收敛性进行分析和证明，给出算法的收敛判据并证明当满足收敛条件时必存在唯一平衡收敛点，同时给出收敛误差方程。

Fri particular, the Wittmann-type strong law of larg numbers for independent random variables is generalized to the case of NA random variables. We also present the sufficient and necessary condition of the laws of logarithm, and we extend Teicher-type strong law of the large numbers for sequence of NA random variables. Some of the laws of iterated logarithm of Teicher-type, Egorov-type arid Wittmann-type for sequence of NA random variables are obtained. Then we investigate the rate3f ionvergcll( fbr series of NA randonl variables, we obtain soIne results fbr tl1e Iaws of theiterated logarithttl, the laws of logarithm and decreasing order fOr the tail sum.Risk itllttlysis tlleory is a sigIlifica11t part of insurance InatheInatics.

Wittmann（1985a）关于实独立随机变量列的结果，并给出了NA列强大数律成立的若干条件，特别建立了一般NA列对数律成立的充分必要条件，在二阶矩存在的条件下完整的解决了一般NA列对数律的问题，中文摘要2而已有的一些NA列对数律的结果可以由它推出，给出了NA列的Teiclier型强大数律，表明lbiChCI·（1979）给出的实独立随机变量列的强大数律可以减弱其条件等；建立厂不问分布NA列的Teicfl仪；Egorov，Petrov型有界重对数律，以及加权同分布NA列的有界重对数律，进一步推广了NA列的Kolmogory有界重对数律等，特别对NA列建立了Wittm洲型有界重对数律，而其证明方法与独立情形有很大不同，同时通过反例表明在与独立场合类似的条件下，独立列的Wittmann有界重对数律不能完美的推广到NA歹小惰形；最后研究了NA随机变量级数的收敛速度，给出了尾和下降的阶；尾和的有界重对数律，及尾和对数律成立的充要条件等，并通过反例说明 NA随机变量级数与独立随机变量级数在收敛速度方面存在的差异。

Under weaker conditions without the strict com-plementarity,the new algorithm still possesses global convergence,strong convergence,superlinearconvergence and quadratic convergence rate.

在无严格互补的条件下，仍获得算法的全局收敛性、强收敛性、超线性收敛性及二次收敛速度。

The conclusions are drawed as following:(1) both reinforcement learning moedel and BBAM can fit the information searching behavior on web in given context;(2) faced with a general restriction, which means a strong uncertainty, the decision making behavior of bounded rationality agent should involve completely rationally calculation and routinism without any conscious reflection on the situation;(3) agent's cognition evolution should be directed by consciousness, the convergence is affected by three factors: the initial belief system, the ...

通过实证分析可以得出以下结论：（1）强化模型和BBAM在一定程度、一定条件内都能较好的刻画用户网络文献检索行为；（2）学习的发生是与特定的外部场景相联系的，在通常的非强外在约束下，有限理性个体进行随机决策时，其行为同时包含了有意识的理性计算和无意识的规则遵循；（3）在随机决策过程中，个体可能发生的认知进化应是有意识指导的结果，但收敛的方向却受到至少三个因素的影响：初始的信念状态、对反馈信号的主观判断能力、试错的次数。

If the neighborhood of equation solution is solvable and meets the point- by-point Lipschitz condition, stable boundary could be obtained as the convergence of the difference method is realized, and thus the stability is ensured.

方程解的邻近如果可解并具备逐点Lipschitz条件，则差分法收敛必有稳定界存在，从而差分格式收敛性保证其稳定性，因此可以放弃线性这一重要条件。

To solve it, we turn it into nonsmooth equations, utilizing inexact theory we give an inexact generalized Newtons method and under some mild conditions we prove that it is global convergence and superlinear convergence .

首先将其约束问题的求解转化为非光滑方程组的求解，然后利用不完全求解理论给出了一个非精确的广义牛顿算法，在一定的条件下证明了算法的全局收敛性和局部超线性收敛性并给出了LC~1非线性约束问题的收敛性条件。

An explicit definition of local peak is shown, and the existence condition of local peak is given. It is proved that the GA is constringent at the neighbor of its local peak. The theoretical evidences of some improvements on GA are given, which points out the way forward for improving GAs performance.

关于遗传算法的收敛性研究的一些典型结果包括：文献 [2 ]分析了在种群规模无穷的情况下典型遗传算法的收敛性；文献 [3]证明了典型遗传算法不收敛，而如果对算法采取记录每一代中最佳个体的策略，则改进的算法收敛；文献 [4]提出了一种等价的遗传算法，并给出了收敛条件与收敛速度；文献 [5 ]研

Plunder melds and run with this jewel!

掠夺melds和运行与此宝石！

My dream is to be a crazy growing tree and extend at the edge between the city and the forest.

此刻，也许正是在通往天国的路上，我体验着这白色的晕旋。