A semilocal convergence result for Newton’s method under generalized conditions of Kantorovich

From Kantorovich’s theory we establish a general semilocal convergence result for Newton’s method based fundamentally on a generalization required to the second derivative of the operator involved. As a consequence, we obtain a modification of the domain of starting points for Newton’s method and improve the a priori error estimates. Finally, we illustrate our study with an application to a special case of conservative problems.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.     

The first eigenvalue of Finsler p-Laplacian

The eigenvalues and eigenfunctions of p-Laplacian on Finsler manifolds are defined to be critical values and critical points of its canonical energy functional. Based on it, we generalize some eigenvalue comparison theorems of p-Laplacian on Riemannian manifolds, such as Lichnerowicz type estimate, Obata type theorem and Mckean type theorem, to the Finsler setting. Not only that, the Lichnerowicz type estimate we obtained is even better than the corresponding one in Riemannian geometry.     

基于时间敏感需求及随机完工期的承诺交货期决策

在新的经济形势和高新技术指引下,产品更新换代的速度加快,基于时间的竞争成为供应链竞争的焦点.在制造商为核心的供应链中,产品需求除了与价格有关外,与承诺交货期也有一定关联,且部分客户愿意为快速交货而支付更高价格.当需求与时间及价格具有敏感性且实际完工期服从一定的随机分布时,建立利润最大化及服务水平约束的承诺交货期决策模型,并对模型进行讨论及优化分析.通过算例验证了模型的有效性,通过参数敏感性分析得出的结论是:当客户服务水平达到一定阈值时,最优承诺交货期将发生改变;价格及交货期敏感系数不影响承诺交货期,但影响产品需求及最终利润;最优承诺交货期与单位提早完工成本是反向变动的关系而与单位延迟完工成本是正向变动的关系;随着完工期均值及标准差的不断增大,最优承诺交货期呈上升趋势,利润、市场需求及价格不断下降.     

操作风险计量研究——基于贝叶斯Copula方法

商业银行操作风险的计量存在两个重要的问题,一个问题是损失数据缺乏,另一个问题是各部分之间的风险相关问题.结合Bayes估计和Copula函数解决了上述两个问题并基于中国商业银行操作风险的损失数据对对操作风险的计量进行了实证分析.实证分析的结果表明无论考虑风险相关与否,基于极大似然估计的VaR与基于Bayes估计的VaR具有一定的差距.     

基于时间序列与灰色拓扑的春节火灾损失预测

火灾每年给中国带来了巨大的损失,春节期间的火灾损失更是严重.根据1999-2010年春节期间火灾统计资料,火灾四项指标数据具有时序性以及随机波动性、模糊性.运用时间序列与灰色拓扑预测方法相结合预测春节期间火灾发生规律,且预测出未来3年内的火灾发生情况.结果表明,时间序列预测模型的平均绝对误差较小,且所建立的灰色拓扑预测模型的拟合精度都达到"好"的标准.因此,采用时间序列与灰色拓扑预测模型相结合对春节火灾发生情况进行预测,其结果合理可靠,可供理论研究和消防部门做出相应的预防措施参考,以达到有效控制和预防春节火灾的目的.     

带有分批费用的容量有界的单机平行分批排序问题

考虑的问题是在添加工资费用或包装费用等附加的分批费用下,如何使单机平行分批中总完工时间和分批费用之和达到最小.首先我们假定工件和批处理机都在零时刻到达,工件被成批地进行加工,一旦开始加工就不允许中断,每批的加工时间等于该批中最大的加工时间,而且假设每分一批都产生一个分批费用.然后对具有m个不同的加工时间,批容量有界且为固定值b的情形下目标函数为∑C_j与分批费用之和这一排序问题,利用动态规划的方法给出了多项式时间算法,时间界为O(b²m2m²222^m).     

对偶Brunn-Minkowski不等式的逆

20世纪80年代Milman曾指出:反向Brunn-Minkowski不等式是凸几何的一个深刻的结果.考虑了对偶情况,建立了一个反向的对偶Brunn-Minkowski不等式.进一步,均值积分差的反向对偶Brunn-Minkowski型不等式也被建立.     

一种线性模型的精确转换方法

基于广义范德蒙矩阵的逆矩阵的计算方法,导出线性系统的连续时间模型和离散时间模型在含有重极点时的严格的直接换算关系,其结果可以直接表示为两者的分子多项式系数之间的关系,并且,对不同的调制器具有统一的转换形式.