BB-倾斜模与Hammock

证明存在Hammock位于有限表示型代数A上BB-倾斜模T_A诱导的AR-箭图上和代数B=End(T_A)的AR-箭图上,并用Hammock对BB-倾斜模T_A进行刻画.     

Shadowing and the Viability Kernel Algorithm

The aim of this paper is to derive estimates for the accuracy of the Viability Kernel Algorithm for systems which have shadowing properties. Recently developed shadowing results are applied in order to prove for a certain class of right hand sides that the algorithm has the same convergence properties as fully discretized numerical methods on a finite time interval.     

Averaged Mappings and the Gradient-Projection Algorithm

It is well known that the gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this article, we first provide an alternative averaged mapping approach to the GPA. This approach is operator-oriented in nature. Since, in general, in infinite-dimensional Hilbert spaces, GPA has only weak convergence, we provide two modifications of GPA so that strong convergence is guaranteed. Regularization is also applied to find the minimum-norm solution of the minimization problem under investigation.     

Additive Scaling and the DIRECT Algorithm

In this paper we show that the convergence behavior of the DIviding RECTangles (DIRECT) algorithm is sensitive to additive scaling of the objective function. We illustrate this problem with a computation and show how the algorithm can be modified to eliminate this sensitivity.     

蚂蚁算法和小生境遗传算法的融合

蚂蚁算法是一种新型的模拟进化算法,也是一种随机型智能搜索算法.较为系统的总结了算法的基本理论,分析了其基本算法解决TSP问题的模型,针对蚂蚁算法易出现停滞的缺点,把小生境遗传算法和蚂蚁算法融合,仿真比较实验结果表明优于基本蚂蚁算法.     

基于遗传算法和改进的Dijkstra算法的电缆敷设优化研究

首先结合电缆敷设相关标准建立了基于多种条件限制的电缆敷设优化的多目标规划模型,将分层序列法的思想运用于模型的求解中.将总敷设路线最短作为第一目标,转弯数最少作为第二目标,错层数最少作为第三目标.求解时首先将遗传算法和改进的Dijkstra算法相结合,共同进行第一目标和第二目标的求解;对于第三目标错层数最少,在运用改进的Dijkstra算法得出待敷设路线后,设计了基于贪心准则的贪婪敷设算法来满足错层数最少的要求.最终通过MATLAB编程实现以上思想并分别对30条和100条电缆的敷设进行实例验证.     

Algorithm for the E-prediction

When the first terms of a sequence are given, a method which gives an approximation of the following ones is called a prediction method. Such methods have already been studied by Allan, Gilewicz, Kiwi, Levin, Prévost, Sidi, Trias, Weissmann, etc.In this paper, we focus on the E-prediction which has been defined and studied by Brezinski and Redivo Zaglia.We obtain a new algorithm generating the E-prediction by using the classical and progressive rules of the E-algorithm. The definition of a prediction method being quite weak, we also talk about consistency in order to appraise the specifications of the E-prediction. At last, some results of consistency are given for a particular case of the E-prediction: the Richardson-prediction.     

Basic Feasible Solutions and the Dantzig-Wolfe Decomposition Algorithm

The relationship between solutions of the full master program of the Dantzig-Wolfe decomposition routine and the originally given problem is considered, and a criterion is established which states whether a basic feasible solution of the former corresponds to one of the latter.     

Pseudomonotone operators and the Bregman Proximal Point Algorithm

To permit the stable solution of ill-posed problems, the Proximal Point Algorithm (PPA) was introduced by Martinet (RIRO 4:154–159, 1970) and further developed by Rockafellar (SIAM J Control Optim 14:877–898, 1976). Later on, the usual proximal distance function was replaced by the more general class of Bregman(-like) functions and related distances; see e.g. Chen and Teboulle (SIAM J Optim 3:538–543, 1993), Eckstein (Math Program 83:113–123, 1998), Kaplan and Tichatschke (Optimization 56(1–2):253–265, 2007), and Solodov and Svaiter (Math Oper Res 25:214–230, 2000). An adequate use of such generalized non-quadratic distance kernels admits to obtain an interior-point-effect, that is, the auxiliary problems may be treated as unconstrained ones. In the above mentioned works and nearly all other works related with this topic it was assumed that the operator of the considered variational inequality is a maximal monotone and paramonotone operator. The approaches of El-Farouq (JOTA 109:311–326, 2001), and Schaible et al. (Taiwan J Math 10(2):497–513, 2006) only need pseudomonotonicity (in the sense of Karamardian in JOTA 18:445–454, 1976); however, they make use of other restrictive assumptions which on the one hand contradict the desired interior-point-effect and on the other hand imply uniqueness of the solution of the problem. The present work points to the discussion of the Bregman algorithm under significantly weaker assumptions, namely pseudomonotonicity [and an additional assumption much less restrictive than the ones used by El-Farouq and Schaible et al. We will be able to show that convergence results known from the monotone case still hold true; some of them will be sharpened or are even new. An interior-point-effect is obtained, and for the generated subproblems we allow inexact solutions by means of a unified use of a summable-error-criterion and an error criterion of fixed-relative-error-type (this combination is also new in the literature).     

团队成员选择的模型及算法

本针对组织中组建团队或重组现有团队时的成员选择问题，提出了反映团队成员之间、成员和团队之间关系的群体效用模型，并根据此模型进行团队成员的选择，从而把团队成员选择问题转化为一个组合优化问题。证明了基于群体效用模型进行团队成员选择的问题是NP-hard问题，并且提出了基于ORASP技术和禁忌算法的启发式算法，最后给出了算例。     

一种改进的粒子群优化算法及其算法测试

粒子群算法原理简单、参数少、易于实现,但有时容易陷入局部最优解,收敛速度慢.本文在粒子群算法理论研究的基础上,对算法的初始值选取、惯性权重取值、算法结构进行了改进:首先采用线性惯性递减权重调整,平衡全局搜索和局部搜索的能力;然后通过logistic映射将混沌状态引入到优化变量中,增强搜索空间的遍历性;最后引入遗传算法中的选择、交叉、变异保持了种群的多样性,使其具有不易陷入局部最优的能力.采用六种典型的测试函数,对惯性权重和算法进行了测试和对比分析.结果表明,算法在收敛速度和精度上都有所提高.     

混沌遗传算法对BP神经网络的改进研究

BP神经网络算法是目前应用最广泛的一种神经网络算法,但有收敛速度慢和易陷入局部极小值等缺陷.本文利用混沌遗传算法(CGA)具有混沌运动遍历性、遗传算法反演性的特性来改进BP神经网络算法.该算法的基本思想是用混沌遗传算法对BP神经网络算法的初始权值和初始阈值进行优化.把混沌变量加入遗传算法中,提高遗传算法的全局搜索能力和收敛速度;用混沌遗传算法优化后得到的最优解作为BP神经网络算法的初始权值和阈值.通过实验观察,改进后的结果与普通的BP神经网络算法的结果相比,具有更高的准确率.     

On the Proximal Point Algorithm

Let A be a maximal monotone operator in a real Hilbert space H and let {u _n } be the sequence in H given by the proximal point algorithm, defined by u _n =(I+c _n A)⁻¹(u _n−1−f _n ), ∀ n≥1, with u ₀=z, where c _n >0 and f _n ∈H. We show, among other things, that under suitable conditions, u _n converges weakly or strongly to a zero of A if and only if lim inf  _n→+∞|w _n |<+∞, where w _n =(∑ _k=1 ⁿ c _k )⁻¹∑ _k=1 ⁿ c _k u _k . Our results extend previous results by several authors who obtained similar results by assuming A ⁻¹(0)≠φ.     

Differentiation of the Cholesky Algorithm

Abstract

One way to estimate variance components is by restricted maximum likelihood. The log-likelihood function is fully defined by the Cholesky factor of a matrix that is usually large and sparse. In this article forward and backward differentiation methods are developed for calculating the first and second derivatives of the Cholesky factor and its functions. These differentiation methods are general and can be applied to either a full or a sparse matrix. Moreover, these methods can be used to calculate the derivatives that are needed for restricted maximum likelihood, resulting in substantial savings in computation.     

有向拟阵与贪婪算法

有向拟阵是拟阵的一种有向情形.本文证明了有向拟阵可用贪婪算法进行刻划.     

基于遗传算法与牛顿法的联合算法在地表发射率反演中的应用

结合遗传算法全局高效搜索和牛顿法局部细致搜索的优势,充分利用一种算法的优点弥补另一种算法的不足,进而引入一种基于遗传算法和牛顿法的联合算法,并将联合算法应用于反演地表发射率的函数关系中.结果表明,联合算法中由遗传算法提供的初始值使得牛顿法下降的速度快,且很快趋于稳定,达到精度要求;而由任意初始值提供给牛顿法,目标函数下降到一定阶段后反而有所回升,然后才保持稳定,且经和联合算法迭代相同的次数后,目标函数的值仍然非常大,远远达不到要求.因此,从可行性、计算效率上看,联合算法均优于单纯的牛顿法,是一种性能稳定,计算高效的下降方法.     

基于模拟退火算法的最小一乘回归新算法

最小一乘准则由于其稳健性较好而在工程中得到广泛的应用,但求解最小一乘回归模型系数的算法往往过于复杂或只能用于样本和变量个数较少的情形.本文根据最小一乘的性质,把最小一乘问题变为组合优化问题,将模拟退火算法用在最小一乘模型的求解上,在后面的数值实验中取得了较好的效果。     

On Koetter's Algorithm and the Computation of Error Values

In this article, we show that Koetter's algorithm for decoding one-point codes can compute error evaluator polynomials as well. We also show that the error evaluators do not need to be computed. The updating functions used in Koetter's algorithm can be used to compute error values instead.     

一种改进的投资组合模型的算法和实证分析

目前,在Markowitz的均值-方差模型基础上对含有偏度和交易成本模型的研究较少,结合国内市场数据进行研究并做出三维投资组合有效前沿图像的成果更少。在建立两种在交易成本约束条件下以方差和偏度的线性组合为目标函数的最优投资组合模型之后,利用线性函数逼近,将模型转换成线性规划问题,而且这种逼近程度可以控制。用单纯形法求解以得到最优投资组合。利用国内八个上市公司的数据进行实证分析,做出了三维投资组合近似有效前沿图像,并讨论了目标函数最优值和参数的关系。可以发现,目标函数是期望r和参数m的增函数。     

水质评价中的冗余理论与去余算法

水质评价在算法层面上解决的问题是,通过实现指标隶属度到目标隶属度转换确定目标水体的污染等级;存在的实质性问题是,如何界定隶属度转换不是简单的线性转换以及如何解决隶属度转换的非线性计算.为此,通过计算由隶属度向量表征的分类信息的信息熵、确定指标对水体分类所做贡献的大小;借助区分权概念揭示指标隶属度中包含对目标水体分类的冗余值.以"冗余"为切入点,建立以冗余定理、非线性转换等定理推论为基本内容的冗余理论.用冗余理论界定隶属度转换的非线性,建立基于区分权滤波的去余算法实现隶属度转换.所建模型在指标隶属度只取"0或1"两个数值时,将退化为通常的"加权平均"线性模型.