An Improved Analysis for a Greedy Remote-Clique Algorithm Using Factor-Revealing LPs

Given a positive integer k and a complete graph with non-negative edge weights satisfying the triangle inequality, the remote-clique problem is to find a subset of k vertices having a maximum-weight induced subgraph. A greedy algorithm for the problem has been shown to have an approximation ratio of 4, but this analysis was not shown to be tight. In this paper, we use the technique of factor-revealing linear programs to show that the greedy algorithm actually achieves an approximation ratio of 2, which is tight. This research was supported in part by the National Science Foundation under grant CISE-EI-0305954 and was performed while the first author was at Washington University in St. Louis. A preliminary version of this paper appears in RANDOM-APPROX ’06, volume 4110 of Lecture Notes in Computer Science, pp. 49–60, 2006.     

基于贪婪算法的疾病相关蛋白质子网搜索

各种研究结果不断证明,人类各种常见疾病都属于复杂疾病,是由多基因、多因素、遗传和环境共同作用的结果。借助于高通量生物技术的飞速发展,生物学家建立起了蛋白交互网络,如果借助复杂网络研究的方法,从这些网络中找出与疾病相关的蛋白质子网络,将有助于我们更深入地了解生物体的运作机制。本文提出了一种基于贪婪算法的搜索方法,能够自动地搜索整个网络中的子网或模块,并且能够结合芯片数据同时进行T检验来判断子网络对疾病表型的区分能力。通过计算子网的P值,给出该蛋白质子网络的统计显著性值并进行区分能力排序。运行结果表明,本方法不但能够用于发现已知的疾病蛋白,而且能够对未知的蛋白进行预测,结合生物芯片技术,将会对疾病基因的研究提供有价值的信息。     

基于多维贪婪搜索的人工蜂群算法

人工蜂群算法在多峰高维函数优化问题的求解上取得了较好的结果,但随着函数的复杂度及维数增高,仍存在收敛速度慢、易陷入局部最优等问题。为此,提出一种新的人工蜂群算法。将人工蜂群对食物源的单维贪婪搜索改进为多维贪婪搜索以增强蜂群的搜索能力,避免在个别维度上出现较优解的食物源由于达到更新阈值却被废弃而造成迂回搜索的现象,引入扰动搜索机制避免迭代后期食物源位置在个别维度收敛导致算法陷入局部最优。仿真实验结果表明,该算法能保持深度挖掘和广度搜索上的平衡,在高维函数优化问题求解的收敛速度和计算精度方面表现出较好的性能。     

基于均值移动与贪婪算法的多人脸跟踪

提出一个基于均值移动(Mean Shift)和贪婪算法的多人脸跟踪器.首先建立多个均值移动目标跟踪器以进行多人脸跟踪.结合卡尔曼滤波逐个检测目标并从视频帧中清除已跟踪到的人脸,以解决当多个目标相邻或相互遮挡时相应的跟踪窗口会收敛于最大目标、导致其他目标丢失的难题.引入辅助窗口并根据其纹理信息确定粘连目标的对应.实验结果表明,该多人脸跟踪算法可实现稳健的实时多人脸跟踪.     

Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search

Content-based copy retrieval (CBCR) aims at retrieving in a database all the modified versions or the previous versions of a given candidate object. In this paper, we present a copy-retrieval scheme based on local features that can deal with very large databases both in terms of quality and speed. We first propose a new approximate similarity search technique in which the probabilistic selection of the feature space regions is not based on the distribution in the database but on the distribution of the features distortion. Since our CBCR framework is based on local features, the approximation can be strong and reduce drastically the amount of data to explore. Furthermore, we show how the discrimination of the global retrieval can be enhanced during its post-processing step, by considering only the geometrically consistent matches. This framework is applied to robust video copy retrieval and extensive experiments are presented to study the interactions between the approximate search and the retrieval efficiency. Largest used database contains more than 1 billion local features corresponding to 30000 h of video     

Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm

Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term and obtain a state-of-the-art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm for SVMs, named GS-SVMs, is presented to cope with the overfitting of HM-SVMs without employing the regularization term. The most attractive property of GS-SVMs is that its computational complexity in the worst case only scales quadratically with the size of training samples. Experiments on the large data sets with up to 400 000 training samples demonstrate that GS-SVMs can be faster than LIBSVM 2.83 without sacrificing the accuracy. Finally, we employ statistical learning theory to analyze the empirical results, which shows that the success of GS-SVMs lies in that its early stopping rule can act as an implicit regularization term.      

基于改进LMedS算法和贪心估计的相位立体匹配

为了改善基于相位测量轮廓术的测量系统测量效率低、系统稳定性差等缺陷,提出一种基于改进LMedS算法和贪心估计策略的快速相位立体匹配方法.首先在LMedS算法中引入平差函数模型,通过及时剔除错误样本减少计算成本函数的累加次数,不仅保持了LMedS算法的良好鲁棒性,而且有效地减少了算法的计算量;用贪心估计策略对改进的LMedS算法获得的初始内点集进行优化,剔除相对匹配程度较差的内点,并用优化的内点集求解基本矩阵,进一步提高算法的估计精度和鲁棒性;最后结合相位立体匹配原理对实物进行了三维重建,分析比较了传统的基于摄像机标定结果的相位立体匹配方法与改进方法的性能.实验结果表明,该方法满足三维形貌测量对实时性、鲁棒性和精度的要求,测量数据完整、可靠.     

螺旋结构及梯度分析的图像融合算法*

为了提高图像融合的效果,提出了螺旋结构和梯度分析的图像融合算法。算法首先进行非下采样轮廓波变换,得到一系列高低频子图。然后对低频子图中稀疏表示方法的滑窗模型进行了研究,针对其融合时间较慢的问题,提出了螺旋结构方向模型进行字典学习和稀疏表示,对稀疏系数通过空间频率取大的规则进行低频子图的融合,提高了融合效率;又针对高频子图中待融合图像的边缘突变情况,提出基于梯度分析的高频融合规则,使得较清晰的图像特征在融合时更易保留至最终的融合图像中。最后,对灰度图像和彩色图像进行了融合实验及不同融合算法的比较分析,并通过主观观察和客观数据对比验证了该算法在时间上和融合效果上的有效性。     

K-median问题贪心近似算法的分析与实验

讨论K-median问题的贪心近似算法及其在实际计算中的表现。提出一个解K-median问题的贪心算法,证明该算法的近似度为O(ln(n/k)),通过实验证明该贪心算法在实际应用当中可以取得较好的效果,大约有90%的客户能被距离其最近、次近和第三近的设备服务。     

A Probabilistic Algorithm for k -SAT Based on Limited Local Search and Restart

A simple probabilistic algorithm for solving the NP-complete problem k -SAT is reconsidered. This algorithm follows a well-known local-search paradigm: randomly guess an initial assignment and then, guided by those clauses that are not satisfied, by successively choosing a random literal from such a clause and changing the corresponding truth value, try to find a satisfying assignment. Papadimitriou [11] introduced this random approach and applied it to the case of 2-SAT, obtaining an expected O(n ² ) time bound. The novelty here is to restart the algorithm after 3n unsuccessful steps of local search. The analysis shows that for any satisfiable k -CNF formula with n variables the expected number of repetitions until a satisfying assignment is found this way is (2? (k-1)/ k) ⁿ . Thus, for 3-SAT the algorithm presented here has a complexity which is within a polynomial factor of (\frac 4 3 ) ⁿ . This is the fastest and also the simplest among those algorithms known up to date for 3-SAT achieving an o(2 ⁿ ) time bound. Also, the analysis is quite simple compared with other such algorithms considered before.     

Greedy Neighborhood Search for Disaster Relief and Evacuation Logistics

Two major activities in disaster response are evacuation and logistics support. Evacuation from affected areas to medical centers occurs during the initial response phase. Logistics support from supply centers to distribution centers in affected areas tends to continue for a longer time to sustain the basic needs of survivors remaining there. Minimizing delay in providing priority commodities and healthcare to the survivors can greatly improve the survival rate. As a first step toward solving the emergency-logistics problem, we developed Path-Builder, a simple, fast constructive heuristic for identifying a feasible, acceptable solution. Path-Builder is based on a greedy l-neighborhood search technique that extends the definition of neighborhood to suit the problem's special needs. The heuristic selects partial paths to append to vehicle itineraries on the basis of the vehicles' utilities. It constructs all vehicle itineraries in parallel and iteratively, trying to exploit foreseeable opportunities within the vehicles' limited neighborhood.     

基于连续消除的六边形自适应搜索算法

结合连续消除算法提出了一种新的运动估计算法——基于连续消除的六边形自适应搜索算法，该算法采用六边形和小菱形搜索模板，并利用连续消除算法对每个待匹配点进行判断，减少待匹配点的个数。试验结果表明，该算法的搜索速度优于现有的快速运动估计算法，搜索精度接近于全搜索算法。     

求解多车型校车路径问题的混合集合划分的GRASP算法

针对不同规划场景下具有不同优化目标的多车型校车路径问题(HSBRP),提出一种混合集合划分(SP)的贪婪随机自适应(Greedy Randomized Adaptive Search Procedure,GRASP)算法。根据GRASP算法寻优过程中产生的路径信息构建SP模型,然后使用CPLEX精确优化器对SP模型进行求解。为了适应不同类型的HSBRP问题,改进GRASP的初始解构造函数得到一个可行解,并将其对应的路径放入路径池;在局部搜索过程中应用多种邻域结构和可变邻域下降(VND)来提升解的质量,同时在路径池中记录在搜索过程中得到提升的路径和在每次迭代中得到局部最好解的路径信息。使用基准测试案例进行测试,实验结果表明在GRASP算法中,混合SP能够有效地提高算法的求解性能和稳定性,并且该算法能适应不同优化目标下车型混合和车辆数限制两类HSBRP的求解;与现有算法的比较结果再次验证了所提算法的有效性。     

基于概率查询算法的P2P仿真平台实现研究

随着P2P网络技术的广泛应用,运用仿真技术来模拟研究P2P网络的运行,已经成为分析研究P2P网络的重要方法。该文实现了一种基于概率查询算法的P2P网络仿真系统。实验表明,运用概率查询算法的仿真系统在查找效率上大大优于传统的洪泛查找方法。     

基于贪婪策略的克鲁斯卡尔算法的实现与分析

贪婪策略可用于求解图的最小生成树,克鲁斯卡尔算法是实现图的最小生成树的一种常用的算法。该文介绍克鲁斯卡尔算法的实现方法,并对算法的运行效率进行分析。     

基于Bloom Filter和概率分发队列的P2P网络快速查找算法

无结构化P2P网络资源定位过程中的响应时间、查准率及覆盖率难以同时被优化。提出一种面向有向无环随机网络的基于Bloom Filter和概率分发队列的快速查找算法BFPDQ(Bloom Filter and Probabilistic Distribution Queue),它用Bloom Filter表达和传递节点命中资源信息及查找请求信息,计算新查询消息与历史查询消息Bloom Filter语义向量相似度,并应用底层网络路径性能信息指导上层转发决策。概率分发队列(Probabilistic Distribution Queue,PDQ)把传统walkers表示成为查找消息分发队列,查找请求者协调各分发队列的查找方向和深度,并融合各队列查找过程中得到的定位消息。仿真实验表明,BFPDQ算法在保持较少冗余信息的同时有效缩短了响应时间。     

基于内容过滤的个性化搜索算法

传统信息检索技术满足了人们一定的需要,但由于其通用的性质,仍然不能满足不同背景、不同目的和不同时期的查询请求.提出了一种基于内容过滤的个性化搜索算法.利用领域分类模型上的概率分布表达了用户的兴趣模型,然后给出了相似性计算和用户兴趣模型更新的方法.对比实验表明,概率模型比矢量空间模型更好地表达了用户的兴趣和变化.     

利用舍伍德算法实现线性表的快速查找

舍伍德算法是概率算法的一种,该文在比较了线性表的顺序存储与链式存储的特点之后,提出了一种较优的数据结构——用数组模拟链表。理论上证明了采用舍伍德算法进行查找运算的时间复尔度为O(n^12),并在计算机上给出了相应数据的模拟。