A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Annealing adaptive search,cross‐entropy,and stochastic approximation in global optimization

The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

一种基于核心数据区监视的交叉验证方法

通常商用体系结构模拟器不开源,仿真过程中无法直接获取中间结果.提出了一种基于核心数据区监视的交叉验证方法,可将体系结构模拟器运行过程中核心数据区的变化情况提取出来,用以支持该模拟器与寄存器传输级代码的模拟结果进行交叉比对,快速精确定位两者的执行差异,提高验证人员的查错效率.实验分析和实际应用表明,本方法比传统方法可将查...     

一种改进的旋转CRC数据校验设计方法

旋转CRC同时使用两个生成多项式产生校验数据。之前的方法校验能力弱,报文丢失后的检错失效率很高。提出了一种新的旋转CRC设计方法,通过比较,选取合适的更高次的生成多项式组合,并且修改了检验生成与检测机制,形成MR-CRC。FPGA实现结果表明,这种方法能够在较低逻辑复杂度的基础上提高校验能力,从而改善数据通信的可靠性,而且对系统性能影响甚小。通过比较16位MR-CRC与32位传统CRC的实现数据发现,前者在所用资源减少10%的情况下,频率提高了25%。     

LRE试车数据挖掘中基于最大散度差的模糊聚类分析方法

在对液体火箭发动机试车数据进行聚类分析时,为解决故障数据样本与正常样本类间差异不大的问题,引入最大散度差准则,提出基于最大散度差的聚类算法MSD-CA.该算法以散度度量样本间的相似性,使样本的类内散度最小化和类间散度最大化同时进行.在此基础上,应用模糊理论对最大散度差准则进行模糊化,提出基于最大散度差的模糊聚类算法MS...     

面向对象的战略战役指挥训练模拟系统数据建模

大型的战略战役指挥训练模拟系统需要内容复杂、数量庞大的数据作为支撑,而结构清晰、容易理解的数据模型是系统成功开发和应用的前提。采用面向对象的基本方法,给出了系统数据建模的概念定义并采用UM L表示法进行了建模方法分析,最后讨论了OO数据模型在系统工程化的数据准备以及运行时和事后分析等应用中所具有的特点。     

一种新的避免航迹合并的比例联合概率关联算法

通过对传统的联合概率数据关联算法(JPDA)在平行临近及小角度交叉目标的关联跟踪中引起的航迹合并问题的分析,提出了一种基于最大交互概率的改进比例联合概率数据关联算法.该算法找出距离两逼近目标的交互概率中心最近的有效观测,引入一比例因子对该观测与所有目标的关联概率进行修正,将逼近目标“拉开”,从而抑制了航迹的合并.仿真结...     

目标多特征信息的模糊数据关联算法

传统的解决密集杂波环境中多目标跟踪的数据关联算法主要是基于目标的距离特征。由于利用的信息有限,容易导致目标的误跟和失跟。提出一种基于目标多特征信息的数据关联算法,将目标的距离特征、方位特征和雷达散射截面积特征进行模糊信息融合,实现对雷达目标的关联。仿真结果表明了该算法的有效性。     

Polaris,East of Suez: British Plans for a Nuclear Force in the Indo-Pacific, 1964–1968

This article investigates the little-known plans formulated by Harold Wilson's Labour government to deploy Polaris submarines in the Indo-Pacific region. The scheme was first proposed in 1965 as a response to several problems faced by British policy-makers, including China's acquisition of a nuclear capability, Britain's wish to maintain a meaningful position ‘East of Suez’ at reduced cost, and German pressure for equal treatment within NATO on nuclear matters. Despite extensive high-level discussion, the plans were finally abandoned in mid-1968, as Labour moved more decisively to forsake the world role.     

French war plans, 1914: The ‘Balance of Power Paradox’

Historians have noted that both German and French war preparation in 1914 fell victim to the inadequacies of traditional threat-based planning: vulnerability to ‘threat deception’ which caused each to underestimate or mischaracterize the threat; a tendency to ‘mirror-image’ by fitting intelligence into preconceived notions of how the enemy was expected to behave; and ‘group think’ that discouraged a serious consideration of alternative scenarios. This article applies the ‘Balance of Power Paradox’ to explain why, at the dawn of the twentieth century, war planning in both Germany and France was driven by an acute sense of weakness which encouraged each side to fashion highly ‘risk acceptant’ strategies. In particular, he examines why and how French commander-in-chief General Joseph Joffre evolved and rationalized his audacious, and disastrous, Plan XVII to leverage French weaknesses and prevent the stronger German Army from bringing the full weight of its military strength to bear against France. The potential implication of this historical vignette is that leaders, and by extension military planners, of both strong and weak states focus on the constraints faced by their opponents, and assume that they can avoid the limitations of their position, while their opponent cannot.