D-最优设计的分部搜索数值构造方法

为了简化D-最优设计的过程,便于工程应用,提出了一种D-最优设计的数值构造方法——分部搜索数值构造方法。该方法以惯性器件测试为背景,以D-最优性为准则,将传统的D-最优设计分解为谱点寻优和测度寻优两个部分,降低了设计的复杂度,从而减少了运算量,又具有操作简单、思路清楚直观的特点,工程实用性强。提出并证明了相关的定理及推论,从理论上说明了这种构造方法的正确性。以陀螺加速度计误差模型系数辨识试验为例,运用此算法进行了D-最优设计。设计的结果证明了该方法的正确性,同直接搜索法相比,能将最小设计维数降低为原来的50%,提高了设计效率。     

强(A,η)-增生算子及其在变分包含中的应用

本文引入一类广义增生算子——强(A,η)-增生算子.定义强(A,η)-增生算子的广义预解算子并证明它的Lipschitz连续性,进一步证明含强(A,η)-增生算子的变分包含的一些新的迭代算法的收敛性.所得结果改进和推广了许多文献的相应结果.     

基于混沌粒子群算法的Tikhonov正则化参数选取

Tikhonov正则化方法是求解不适定问题最为有效的方法之一,而正则化参数的最优选取是其关键.本文将混沌粒子群优化算法与Tikhonov正则化方法相结合,基于Morozov偏差原理设计粒子群的适应度函数,利用混沌粒子群优化算法的优点,为正则化参数的选取提供了一条有效的途径.数值实验结果表明,本文方法能有效地处理不适定问题,是一种实用有效的方法.     

Single machine scheduling with semi-resumable machine availability constraints

This paper considers the semi-resumable model of single machine scheduling with a non-availability period. The machine is not available for processing during a given time interval. A job cannot be completed before the non-availability period will have to partially restart after the machine has become available again. For the problem with objective of minimizing makespan, the tight worst-case ratio of algorithm LPT is given, and an FPTAS is also proposed. For the problem with objective of minimizing total weighted completion time, an approximation algorithm with worst-case ratio smaller than 2 is presented. Two special cases of the latter problem are also considered, and improved algorithms are given.     

科技保险中项目投资损失保险投保比例优化决策

针对我国科技保险第二批推行险种--项目投资损失保险,以科技企业为研究主体,综合考虑其期望利润和科技风险(方差),构建了投保比例模型.在对武汉市迪源光电科技有限公司投保科技保险的具体案例中,运用线性不等式组的旋转算法进行求解,计算出企业在项目组合投资中如何优化各项投保比例,使其以最小的风险承担得到最大的期望利润.     

一类自由作业供应链排序的研究

本文研究一类集成工件生产和发送的排序模型.在该模型中,供应链的上游首先将工件安排在自由作业机器上加工,然后把加工完毕的工件分批发送给下游.问题是寻找生产和发送相连的排序,使得生产排序费用和发送费用总和最少.这里,生产排序费用是以工件带权送到时间和表示;发送费用由固定费用和与运输路径有关的变化费用组成.在指出问题的NP困难性后,本文用动态规划算法构造了一致条件下的多项式时间近似算法,并分析算法的性能比.本文最后还讨论了该问题的其它情形.     

改进的GM（1,1）幂模型及其参数优化

为了提高灰色GM（1,1）幂模型的拟合精度,对灰色GM（1,1）幂模型的背景值进行了改进,建立了一类改进GM（1,1）幂模型.利用粒子群优化算法给出了改进GM（1,1）幂模型的参数优化.实例分析结果表明基于粒子群算法的改进的GM（1,1）幂模型具有更高的预测和拟合精度.     

A lower bound guaranteeing exact matrix completion via singular value thresholding algorithm

In this paper, we give a lower bound guaranteeing exact matrix completion via singular value thresholding (SVT) algorithm. The analysis shows that when the parameter in SVT algorithm is beyond some finite scalar, one can recover some unknown low-rank matrices exactly with high probability by solving a strictly convex optimization problem. Furthermore, we give an explicit expression for such a finite scalar. This result in the paper not only has theoretical interests, but also guides us to choose suitable parameters in the SVT algorithm.     

A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.     

Subgroup analysis of zero-inflated Poisson regression model with applications to insurance data

Customized personal rate offering is of growing importance in the insurance industry. To achieve this, an important step is to identify subgroups of insureds from the corresponding heterogeneous claim frequency data. In this paper, a penalized Poisson regression approach for subgroup analysis in claim frequency data is proposed. Subjects are assumed to follow a zero-inflated Poisson regression model with group-specific intercepts, which capture group characteristics of claim frequency. A penalized likelihood function is derived and optimized to identify the group-specific intercepts and effects of individual covariates. To handle the challenges arising from the optimization of the penalized likelihood function, an alternating direction method of multipliers algorithm is developed and its convergence is established. Simulation studies and real applications are provided for illustrations.