无穷范数松弛的低复杂度超奈奎斯特检测

超奈奎斯特(Faster-than-Nyquist,FTN)速率传输可以有效提高频谱效率,但这种非正交传输方式引入的严重码间串扰相应提高了接收端的处理难度。针对该问题,设计了一种基于循环成块传输的低复杂度检测算法。最优检测被建模为无约束的二元二次规划(Boolean Quadratic Program,BQP)问题,为了求解该NP-hard问题,采用无穷范数约束松弛原问题的非凸可行解集,并基于次梯度下降法提出松弛问题的有效优化算法。数值仿真结果表明,所提算法在误比特率(Bit Error Rate,BER)性能上优于频域均衡,且在可接受的性能损失范围内算法执行效率远高于理论最优的最大似然序列估计(Maximum Likelihood Sequence Estimation,MLSE)。     

A genetic algorithm extended modified sub-gradient algorithm for cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routes. The problem is considered in the aspect of the natural constraints of real-life production systems such as cell size, separation and co-location constraints. Co-location constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and separation constraints were included to prevent placing certain machines in close vicinity. The objective is to minimise the weighted sum of the voids and the exceptional elements. A hybrid algorithm is proposed to solve this problem. The proposed algorithm hybridises the modified sub-gradient (MSG) algorithm with a genetic algorithm. MSG algorithm solves the sharp augmented Lagrangian dual problems, where zero duality gap property is guaranteed for a wide class of optimisation problems without convexity assumption. Generally, the dual problem is solved by using GAMS solvers in the literature. In this study, a genetic algorithm has been used for solving the dual problem at the first time. The experimental results show the advantage of combining the MSG algorithm and the genetic algorithm. Although the MSG algorithm, whose dual problem is solved by GAMS solver, and the genetic algorithm cannot find feasible solutions, hybrid algorithm generates feasible solutions for all of the test problems.     

一种改善小波变换模极大值重构信号的整体变分方法

在小波变换模极大值去噪原理的基础上,通过整体变分法估计模极大值以外、非零值的小波系数,然后经过小波逆变换重构信号.为求解这个极小化问题,采用了改进的次梯度方法.数值计算结果表明:我们提出的方法有效抑制了小波变换中固有的伪Gibbs现象,重构信号的边缘、脉冲位置都十分准确,信噪比也得到明显改善.     

WSN中基于对偶分解和次梯度策略的能耗管理方案

为了减弱充电速率的时间可变性对能量收集传感器网络的影响,文中提出一种基于高效对偶分解和次梯度策略的算法Quick-Fix来计算数据采样率和路由;另外,为了应对因充电率发生波动所造成的电池断电、溢出、采样丢失和能量收集机会丢失等情况,提出一种本地算法SnapIt,通过对采集率进行调节以维持电池电量在目标水平上;基于TOSSIM模拟器的性能评估表明,联合QuickFix和SnapIt可跟踪网络瞬时最优效用,同时维持电池电量处于目标水平;与基于余压的IFRC相比,文中方法使总体数据速率平均提升42％,同时显著提升了网络效用.     

带最小批量约束的计划问题及其拉格朗日松弛算法

针对一类带最小批量约束的计划问题, 提出了基于拉格朗日松弛策略求解算法. 通过拉格朗日松弛策略, 将原问题转为一系列带最小批量约束的动态经济批量W-W(Wagner-Whitin)子问题. 提出了解决子问题且其时间复杂度O(T³)的最优前向递推算法. 对于拉格朗日对偶问题, 用次梯度算法求解, 获得原问题的下界. 若对偶问题的解是不可行的, 通过固定装设变量, 求解一个剩余的线性规划问题来进行可行化处理. 最后, 数据仿真验证了算法的有效性.     

线性及非线性一致性问题综述

本文综述多自主体系统线性及非线性一致性问题.首先,从自主体动态、通信拓扑和一致性协议这3个方面介绍一致性问题的基本问题.接着,从一致性问题的分析方法入手,对目前一致性问题的研究结果进行分类和讨论.然后阐释经典一致性协议是负的次梯度算法这一实质,并给出一种一致性协议的设计方法.最后列举了仍然需要解决的问题和未来的研究方向.     

红水河梯级水电站开发中火电站程序优化模型与算法

本文运用解组合最优化问题的拉格朗日松弛法研究电力系统中火电站排序问题,阐述了基本思想,给出了火电站排序的理想模型和实际模型,讨论了拉格朗日松弛法的具体运用,并给出了实际排序模型的拉格朗日松弛法计算框图。