Banach空间中φ-渐进非扩展映像不动点的迭代构造

在Mann的迭代算法基础上，运用Banach空间中的广义投影，使渐进非扩展映像每次迭代生成的序列都投影到一个闭凸的集合中．并证明了该算法的强收敛性．     

在退化情况下的一个可行方向法

本文给出了一个新的非线性约束优化的可行方向法．该算法适用于退化问题(积极约束梯度线性相关)，算法结构简单，在适当条件下，证明此算法具有全局收敛性．数值实验表明算法是有效的．     

不等式约束优化拟正则条件下的超线性收敛FSSLE算法

对不等式约束优化问题。提出一个可行序列线性方程组（FSSLE）算法。该算法每次迭代只需求解两个具有相同系数矩阵的线性方程组,因而计算量较小。在一定条件下,算法具有全局收敛性。在没有严格互补条件、比强二阶充分条件弱的拟正则条件下,证明了算法具有超线性收敛性并用数值试验表明其有效性。     

不等式约束优化的一个超线性收敛FSSLE算法

本文针对不等式约束优化问题,提出了一个可行序列线性方程组(FSSLE)算法.该算法每次迭代只需求解四个具有相同系数矩阵的线性方程组,因而计算量较小.在没有假设算法产生的聚点是孤立点和近似乘子列有界的条件下,证明了算法具有全局收敛性.在一般条件下,证明了算法具有超线性收敛性.     

解一类结构变分不等式问题的非精确并行交替方向法

带线性约束的具有两分块结构的单调变分不等式问题, 出现在许多现代应用中, 如交通和经济问题等. 基于该问题良好的可分结构, 分裂型算法被广泛研究用于其求解. 提出新的带回代的非精确并行交替方向法解该类问题, 在每一步迭代中,首先以并行模式通过投影得到预测点, 然后对其校正得到下一步的迭代点. 在压缩型算法的理论框架下, 在适当条件下证明了所提算法的全局收敛性. 数值结果表明了算法的有效性. 此外, 该算法可推广到求解具有多分块结构的问题.     

非线性约束条件下一个超线性收敛的可行方法

在本文中，我们对非线性不等式约束条件下的非线性优化问题给出了一个新的ＳＱＰ类可行方法．此算法不但结构简单、易于计算，并且在适当的假设条件下，我们证明了算法具有全局收敛性及超线性收敛性     

随机ADMM算法及其在电力系统凸经济调度问题中的应用

针对电力系统中的一类凸经济调度问题,提出了随机ADMM算法,设计了周期循环更新规则和随机选择更新规则,证明了随机ADMM算法在周期循环更新规则下的收敛性,以及得出了在随机选择更新规则下按期望收敛的结论.数值实验结果表明该方法可以有效解决电力系统中的凸经济调度问题.     

适应性波束形成器的随机变界截尾算法

本文对适应性波束形成器给出一种递推随机变界截尾算法,并在较简单的条件下,证明了算法几乎处处具有大范围收敛性.     

一种具有全局收敛性的求解二阶锥规划的非精确光滑算法

在方程组方法框架下,给出了一种求解二阶锥规划的非精确光滑算法.在适当的条件下,证明了该算法具有全局收敛性.数值试验表明该算法对求解中大规模二阶锥规划是有效的.     

线性二阶锥互补问题的一种非精确光滑算法

在光滑算法的框架下,就线性二阶锥互补问题,给出了一种非精确光滑算法. 在适当的条件下,证明了该算法具有全局收敛性. 数值试验表明该算法对高维线性二阶锥互补问题是有效的.     

Extrapolated Smoothing Descent Algorithm for Constrained Nonconvex and Nonsmooth Composite Problems*

In this paper, the authors propose a novel smoothing descent type algorithm with extrapolation for solving a class of constrained nonsmooth and nonconvex problems,where the nonconvex term is possibly nonsmooth. Their algorithm adopts the proximal gradient algorithm with extrapolation and a safe-guarding policy to minimize the smoothed objective function for better practical and theoretical performance. Moreover, the algorithm uses a easily checking rule to update the smoothing parameter to ensure that any accumulation point of the generated sequence is an (affine-scaled) Clarke stationary point of the original nonsmooth and nonconvex problem. Their experimental results indicate the effectiveness of the proposed algorithm.     

Does coveting the performance of neighbors of thy neighbor enhance spatial reciprocity?

Understanding the emergence and prevalence of cooperation among selfish agents is a long-standing puzzle in the scientific community, various mechanisms and models have been proposed to account for the cooperative behaviors. In this work, we present a modified strategy update dynamics in which the environmental information is added into the standard Fermi rule. Extensive numerical simulations have demonstrated that cooperation can be strongly promoted under our novel update dynamics. Our results also indicate that referring the environmental information during the strategy update is helpful for fostering the cooperative clusters and avoiding to be exploited by the defectors. Our results provide further insights into competition and cooperation, and contribute to explain the collective cooperation in many real systems.     

A NOTE ON THE GRADIENT PROJECTION METHOD WITH EXACT STEPSIZE RULE

In this paper, we give some convergence results on the gradient projection method with exact stepsize rule for solving the minimization problem with convex constraints. Especially, we show that if the objective function is convex and its gradient is Lipschitz continuous, then the whole sequence of iterations produced by this method with bounded exact stepsizes converges to a solution of the concerned problem.     

多目标0-1规划问题的元胞狼群优化算法研究

针对多目标0-1规划问题,首先基于元胞自动机原理和人工狼群智能算法,提出一种元胞狼群优化算法,该算法将元胞机的演化规则与嚎叫信息素更新规则、人工狼群更新规则进行组合,采用元胞及其邻居来增强搜索过程的多样性和分布性,使人工头狼在元胞空间搜索的过程中,增强了人工狼群算法的全局搜索能力,并获得更多的全局非劣解;其次结合多目标0-1规划模型对元胞狼群算法进行了详细的数学描述,定义了人工狼群搜索空间、移动算子、元胞演化规则和非劣解集更新规则,并给出了元胞狼群算法的具体实现步骤;最后通过MATLAB软件对3个典型的多目标0-1规划问题算例进行解算,并将解算结果与其它人工智能算法的结果进行比较,结果表明:元胞狼群算法在多目标0-1规划问题求解方面可获得更多的非劣解集和更优的非劣解,并具有较快的收敛速度和较好的全局寻优能力。     

Conditioning (updating) non-additive measures

Several update rules for non-additive probabilities, among them the Dempster-Shafer rule for belief functions and certain update rules in the spirit of Bayesian statistics with multiple prior probabilities, are reviewed, investigated and compared with each other. This is done within the unifying framework of general, non-additive measure and integration theory. The methods exposed here are capable of generalizing conditional expectation of random variables to the sub-modular or supermodular case at least if the given algebra is finite.     

A Switching Algorithm Based on Modified Quasi-Newton Equation

In this paper, a switching method for unconstrained minimization is proposed. The method is based on the modified BFGS method and the modified SR1 method. The eigenvalues and condition numbers of both the modified updates are evaluated and used in the switching rule. When the condition number of the modified SR1 update is superior to the modified BFGS update, the step in the proposed quasi-Newton method is the modified SR1 step. Otherwise the step is the modified BFGS step. The efficiency of the proposed method is tested by numerical experiments on small, medium and large scale optimization. The numerical results are reported and analyzed to show the superiority of the proposed method.     

GERT Analysis of Least-Cost Testing Sequence

An analysis of least-cost testing sequence by Graphical Evaluation and Review Technique (GERT) is given for an extension of Boothroyd's rule. The method based on this analysis yields easily mean and variance of the total cost including testing, reworking, and scrapping cost. An optimal rule for determination of testing sequence which minimizes the mean of the total cost is illustrated. If some sequences have the equal minimum mean cost, the rule enables the optimal sequence which has the smaller variance of the total cost to be chosen.     

An efficient shift rule for the prefer-max De Bruijn sequence

A shift rule for the prefer-max De Bruijn sequence is formulated, for all sequence orders, and over any finite alphabet. An efficient algorithm for this shift rule is presented, which has linear (in the sequence order) time and memory complexity.     

An iterative algorithm for parametrization of shortest length linear shift registers over finite chain rings

The construction of shortest feedback shift registers for a finite sequence \(S_1,\ldots ,S_N\) is considered over finite chain rings, such as \({\mathbb Z}_{p^r}\). A novel algorithm is presented that yields a parametrization of all shortest feedback shift registers for the sequence of numbers \(S_1,\ldots ,S_N\), thus solving an open problem in the literature. The algorithm iteratively processes each number, starting with \(S_1\), and constructs at each step a particular type of minimal basis. The construction involves a simple update rule at each step which leads to computational efficiency. It is shown that the algorithm simultaneously computes a similar parametrization for the reverse sequence \(S_N,\ldots ,S_1\). The complexity order of the algorithm is shown to be \(O(r N^2)\).