Generating cryptographically strong Boolean functions using partial information

Periodica Mathematica Hungarica - In our paper we study the usage of partially defined Boolean functions (PDBFs) for generating cryptographically strong Boolean functions. A PDBF can be considered...     

Combining Swaps and Node Weights in an Adaptive Greedy Approach for the Maximum Clique Problem

In this work, the NP-hard maximum clique problem on graphs is considered. Starting from basic greedy heuristics, modifications and improvements are proposed and combined in a two-phase heuristic procedure. In the first phase an improved greedy procedure is applied starting from each node of the graph; on the basis of the results of this phase a reduced subset of nodes is selected and an adaptive greedy algorithm is repeatedly started to build cliques around such nodes. In each restart the selection of nodes is biased by the maximal clique generated in the previous execution. Computational results are reported on the DIMACS benchmarks suite. Remarkably, the two-phase procedure successfully solves the difficult Brockington-Culberson instances, and is generally competitive with state-of-the-art much more complex heuristics.     

贪婪格点路径的一些性质

设{Xv:v∈Zd}是一族独立同分布的随机变量序列,对Zd上的任意一路径π,定义S(π)=∑v∈πXv,记Mn=maxπ∈Π0(n)S(π),Π0(n)表示从原点出发大小为n的自不相交的路径的全体.论文讨论Mn的性质,得到了类似完全收敛性的结果,即对任意的ε>0,∑∞n=11nPMnn-M>ε<∞.另外还讨论了Xv允许取负值的情形,得到类似的结果,从而推广了Gandolfi和Kesten所研究的贪婪格点路径模型.     

110警车配置及巡逻方案

针对110警车调度问题,引入了图论中的最短路算法以及计算几何的相关理论,建立了车辆调配模型、巡逻路线模型以及基于模糊数学的评价指标模型.另外,用C++编写了一个可视化的软件,不仅实现了手动描点,自动求出覆盖线段集合的功能,同时利用计算机模拟警车的巡逻路线,最后通过计算机检验得到结果,其合理性和实用性都令人满意.针对问题一,通过人机结合,配置17辆警车就能实现D1的目标,很好地兼顾了警车巡逻的运行成本,减少公安部门车辆和人员等的投入.针对问题二,采用模糊数学相关理论使评价指标实现了从定性到定量的转变.针对问题三和六,在D1的基础上,兼顾了巡逻效果的显著性,采用最少被巡逻道路优先的贪心算法建立了动态巡逻模型,得到了合理的巡逻方案.在此方案中,我们动用了30辆警车完成了问题一的目标.另外还额外考虑了案发事件概率不均匀分布的情况,建立了改进模型.针对问题四,在完成问题三指标的基础上,为了尽可能提高巡逻车辆的隐蔽性和增强巡逻效果,采用轮盘赌算法来引入随机性.针对问题五,采用最远距离道路优先贪心策略,使模型尽可能满足条件D1,D2.针对问题七,提出了一些额外因素及其解决方案,进一步完善了模型,使模型更贴近现实.     

On the greedy solution in integer linear programming

A version of the greedy method not using any knapsack relaxation of the integer programming problem is considered in this paper. It is based on a natural partial ordering of the vectors. Our aim is to determine a large class of problems where the greedy solution is always optimal. The results generalize some theorems of an early paper of Magazine, Nemhauser and Trotter and at the same time show a connection between two different notions of combinatorics: the greedy method and the Hilbert basis.

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Zusammenfassung In dieser Arbeit wird eine Version des Greedy-Algorithmus zur Lösung ganzzahliger linearer Optimierungsprobleme benutzt, die kein Rucksackproblem als Relaxation verwendet. Das Verfahren basiert auf der natürlichen partiellen Ordnung von Vektoren. Ziel der Arbeit ist es, eine möglichst große Problemklasse zu beschreiben, für die die Greedy-Lösung optimal ist. Die Ergebnisse verallgemeinern Sätze einer früheren Arbeit von Magazine, Nemhauser und Trotter und zeigen gleichzeitig einen Bezug zwischen zwei verschiedenen Gebieten der Kombinatorik auf: des Greedy-Verfahrens und von Hubert-Basen.

     

A Study of Permutation Operators for Minimum Span Frequency Assignment Using an Order Based Representation

The genetic algorithm (GA) described in this paper breeds permutations of transmitters for minimum span frequency assignment. The approach hybridizes a GA with a greedy algorithm, and employs a technique called Generalized Saturation Degree to seed the initial population. Several permutation operators from the GA literature are compared, and results indicate that position based operators are more appropriate for this kind of problem than are order based operators. My offspring versus mid-parent correlation studies on crossovers show Pearson's correlation coefficient to be a reliable predictor of performance in most cases. Results presented herein represent improvements over previously published results.     

Locating Multiple Competitive Facilities: Spatial Interaction Models with Variable Expenditures

We develop a new framework for location of competitive facilities by introducing non-constant expenditure functions into spatial interaction location models. This framework allows us to capture two key effects – market expansion and cannibalization – within the same model.We develop algorithmic approaches for finding optimal or near-optimal solutions for several models that arise from choosing a specific form of the expenditure functions.     

A Lower Bound on the Greedy Weights of Product Codes

A greedy 1-subcode is a one-dimensional subcode of minimum (support) weight. A greedy r-subcode is an r-dimensional subcode with minimum support weight under the constraint that it contain a greedy (r - 1)-subcode. The r-th greedy weight e _r is the support weight of a greedy r-subcode. The greedy weights are related to the weight hierarchy. We use recent results on the weight hierarchy of product codes to develop a lower bound on the greedy weights of product codes.     

卫星运动预测及规避的建模与求解

近年来,随着各国侦查卫星系统的不断完善,有效根据观测数据预测卫星的轨迹,同时建立适当的规避防范措施变得尤为重要·通过研究地面监测站监测的卫星数据及地理信息,提出了一种基于三维坐标系变换的卫星轨迹求解模型,具体建立模型的方法为:建立4种不同的坐标系,通过卫星坐标在4种坐标系中的变换,将地面检测站检测到的卫星数据进行处理,拟合出卫星的六个运动参数,得出卫星运动轨迹方程,并较为精准的预测卫星被观测站观测到的情况,以及任意时刻星下点的经纬度坐标.针对卫星的运动情况,为满足我方军事目标的运动需求,根据新疆地图建立军队行军路线的有向图,然后,在考虑卫星规避的情况下对每条路线进行模拟分析,最终,通过每条路线模拟分析的结果进行比对,得出最优的行军路线.     

Variant of Greedy Randomized Gauss-Seidel Method for Ridge Regression

The variants of randomized Kaczmarz and randomized Gauss-Seidel algorithms are two effective stochastic iterative methods for solving ridge regression problems. For solving ordinary least squares regression problems, the greedy randomized Gauss-Seidel (GRGS) algorithm always performs better than the randomized Gauss-Seidel algorithm (RGS) when the system is overdetermined. In this paper, inspired by the greedy modification technique of the GRGS algorithm, we extend the variant of the randomized Gauss-Seidel algorithm, obtaining a variant of greedy randomized Gauss-Seidel (VGRGS) algorithm for solving ridge regression problems. In addition, we propose a relaxed VGRGS algorithm and the corresponding convergence theorem is established. Numerical experiments show that our algorithms outperform the VRK-type and the VRGS algorithms when $m > n$.