Technical note: Finite-time regret analysis of Kiefer-Wolfowitz stochastic approximation algorithm and nonparametric multi-product dynamic pricing with unknown demand

We consider the problem of nonparametric multi-product dynamic pricing with unknown demand and show that the problem may be formulated as an online model-free stochastic program, which can be solved by the classical Kiefer-Wolfowitz stochastic approximation (KWSA) algorithm. We prove that the expected cumulative regret of the KWSA algorithm is bounded above by where κ₁, κ₂ are positive constants and T is the number of periods for any T = 1, 2, … . Therefore, the regret of the KWSA algorithm grows in the order of , which achieves the lower bounds known for parametric dynamic pricing problems and shows that the nonparametric problems are not necessarily more difficult to solve than the parametric ones. Numerical experiments further demonstrate the effectiveness and efficiency of our proposed KW pricing policy by comparing with some pricing policies in the literature.     

并行离散事件仿真中的DDM机制实现

在并行离散事件仿真系统中实现仿真模型间的互操作机制需要引入类似HLA中定义的声明管理(DM)和数据分发管理(DDM)服务.我们开发的KD-PADSE高性能并行离散事件仿真环境采用谓词断言和对象代理方法在面向对象的并行离散事件仿真系统中实现了基于平行结构和事件调度机制的声明管理(DM)和数据分发管理(DDM)服务.在以千兆以太网互联的10节点Linux集群计算机上,采用DDM机制后仿真模型每秒(物理时间)可以进行1万次属性更新,是没有采用DDM机制时的20倍,在功能性和性能上验证了该DDM机制在并行离散事件仿真系统中的适用性.     

A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi‐stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 600–614, 2017     

基于蜂群算法多子阵电台天线波束形成

考虑到战场环境下,低信噪比和敌方干扰容易造成超短波电台通信中断,而鉴于天线阵可以有效提高信噪比和抑制干扰,结合战场电台天线分布特点,提出一种多子阵电台天线应急接收模型。理论分析多子阵电台天线波束形成特性,以消除栅瓣、抑制干扰、提高增益为目标,采用蜂群算法对子阵间距进行优化。仿真结果表明,优化子阵间距可以有效消除栅瓣、抑制干扰、提高增益。同时,与遗传算法和粒子群算法相比,蜂群算法具有更好的全局搜索能力和收敛速度。该研究成果可为超短波电台应急通信提供参考。     

随机共振理论在雷达信号截获中的应用

雷达侦察主要通过无源侦察接收设备对空域中敌方雷达辐射源信号进行检测,若要实现对敌辐射源信号的截获,基本条件就是敌雷达信号能量能够达到雷达信号侦收设备的截获灵敏度。然而现代战场中,电磁环境复杂多变,敌雷达信号往往被干扰信号、噪声等掩盖,导致侦收设备漏警概率提高。针对以上问题,提出基于随机共振原理的雷达信号截获判断的方法,增大了截获信号的输出信噪比,解决了由于噪声过大掩盖的雷达信号的侦收问题。将该方法应用于雷达告警接收机的前端截获部分,大大降低了截获系统的漏警概率,保证了整个截获系统的稳定性。仿真实验验证了该方法的有效性。     

一种运用遗传算法确定船舶航向控制模糊规则的方法

为了克服船舶航向控制模糊规则确定过程中的盲目性,引入一种与之相适应的遗传算法,以使结果成为某种意义下的最优解,从而从根本上解决这一重要问题.文中运用Matlab的C语言MEX-文件技术,将遗传算法源代码与Matlab直接结合起来,利用后者的强大建模能力,建立起完整的仿真模型.仿真结果表明,船舶的航向控制能力得到明显改善.     

As-IPDA算法的天波超视距雷达目标跟踪研究

天波超视距雷达(OTHR)具有低数据率、低检测概率和低量测精度特征对目标跟踪提出挑战.针对OTHR在低检测概率下跟踪目标存在失跟率高的问题,将As-IPDA算法应用到OTHR目标跟踪中,通过大量的仿真实例验证了As-IPDA算法在超视距雷达目标跟踪应用中的可行性,并分析了平滑步长、航迹存在性转移参数、目标检测概率对算法性能的影响,给出了算法参数选择建议,为工程应用提供理论参考.     

基于分形的地图自动综合方法

为了较好地解决根据已知比例尺的地图通过自动综合生成满足不同比例尺需求的地图，利用地图边界所具有的分形特征及分形维数的不变性，提出了一种地图边界自动综合的方法——MADP。该方法不仅很好地保持了地图的形状特征和细节，而且较好地解决了公共边界的综合问题。实验表明，该方法具有较高的效率和公共边界综合能力。     

An integer decomposition algorithm for solving a two‐stage facility location problem with second‐stage activation costs

We study a stochastic scenario‐based facility location problem arising in situations when facilities must first be located, then activated in a particular scenario before they can be used to satisfy scenario demands. Unlike typical facility location problems, fixed charges arise in the initial location of the facilities, and then in the activation of located facilities. The first‐stage variables in our problem are the traditional binary facility‐location variables, whereas the second‐stage variables involve a mix of binary facility‐activation variables and continuous flow variables. Benders decomposition is not applicable for these problems due to the presence of the second‐stage integer activation variables. Instead, we derive cutting planes tailored to the problem under investigation from recourse solution data. These cutting planes are derived by solving a series of specialized shortest path problems based on a modified residual graph from the recourse solution, and are tighter than the general cuts established by Laporte and Louveaux for two‐stage binary programming problems. We demonstrate the computational efficacy of our approach on a variety of randomly generated test problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

一种BP神经网络算法的改进模型

首先对BP网络的结构和算法进行了分析,针对BP网络收敛速度慢,容易陷入局部极小等问题,提出了一种改进的BP网络模型,并对该模型算法进行了改进,通过激活函数的选择,网络的初始化,学习率的调整和训练样本数据的处理等方法,可实现加快网络的收敛速度,并且较好的解决局部最优问题.