Many-to-Many Communication in Radio Networks

Radio networks model wireless data communication when the bandwidth is limited to one wave frequency. The key restriction of such networks is mutual interference of packets arriving simultaneously at a node. The many-to-many (m2m) communication primitive involves p participant nodes from among n nodes in the network, where the distance between any pair of participants is at most d. The task is to have all the participants get to know all the input messages. We consider three cases of the m2m communication problem. In the ad-hoc case, each participant knows only its name and the values of n, p and d. In the partially centralized case, each participant knows the topology of the network and the values of p and d, but does not know the names of the other participants. In the centralized case, each participant knows the topology of the network and the names of all the participants. For the centralized m2m problem, we give deterministic protocols, for both undirected and directed networks, working in time, which is provably optimal. For the partially centralized m2m problem, we give a randomized protocol for undirected networks working in time with high probability (whp), and we show that any deterministic protocol requires time. For the ad-hoc m2m problem, we develop a randomized protocol for undirected networks that works in time whp. We show two lower bounds for the ad-hoc m2m problem. One lower bound states that any randomized protocol for the m2m ad hoc problem requires expected time. Another lower bound states that for any deterministic protocol for the m2m ad hoc problem, there is a network on which the protocol requires time when n−p(n)=Ω(n) and d>1, and that it requires Ω(n) time when n−p(n)=o(n). The results of this paper appeared in a preliminary form in “On many-to-many communication in packet radio networks” in Proceedings of the 10th Conference on Principles of Distributed Systems (OPODIS), Bordeaux, France, 2006, Lecture Notes in Computer Science 4305, Springer, Heidelberg, pp. 258–272. The work of B.S. Chlebus was supported by NSF Grant 0310503.     

多目标优化问题的有效Pareto最优集

多目标优化问题求解是当前演化计算的一个重要研究方向,而基于Pareto最优概念的遗传算法更是研究的重点,然而,遗传算法在解决多目标优化问题上的缺陷却使得其往往得不到一个令人满意的解。在对该类算法研究的基础上提出了衡量Pareto最优解集的标准,并对如何满足这个标准提出了建议。     

Pareto Optimal Decentralized Control of a Rotor in Electromagnetic Bearings

Journal of Computer and Systems Sciences International - The problem of the optimal stabilization of a vertical rigid rotor rotating in two electromagnetic bearings is considered. A quantitative...     

基于遗传多目标优化算法的Pareto最优解研究

遗传算法可有效求解多目标优化问题中的Pareto最优解,并利用MATLAB进行了仿真验证。     

Three-Sided Stable Matchings with Cyclic Preferences

Knuth (Mariages Stables, Les Presses de L’Université de Montréal, 1976) asked whether the stable matching problem can be generalised to three dimensions, e.g., for families containing a man, a woman and a dog. Subsequently, several authors considered the three-sided stable matching problem with cyclic preferences, where men care only about women, women only about dogs, and dogs only about men. In this paper we prove that if the preference lists may be incomplete, then the problem of deciding whether a stable matching exists, given an instance of the three-sided stable matching problem with cyclic preferences, is NP-complete. Considering an alternative stability criterion, strong stability, we show that the problem is NP-complete even for complete lists. These problems can be regarded as special types of stable exchange problems, therefore these results have relevance in some real applications, such as kidney exchange programs.     

高效求解Pareto最优前沿的多目标进化算法

设计了一种新的求解均匀分布的Pareto最优解集的多目标进化算法(MOEA),其主要的特点是使用了一种新的个体适应值的计算方式,方法是通过群体中某一个体与群体的最优非劣解集的最小距离来刻画个体的适应值的.算法还结合了遗传算法中的精英策略以及NSGA-Ⅱ中的拥挤距离[12],提高了非劣解向Pareto最优前沿收敛的速度,并且保证了Pareto 最优解集的多样性.仿真结果表明,算法不仅能够获得分布良好的Pareto最优前沿,而且能够极大地简化计算,减少了算法的运行时间,其计算复杂度为o(mn2)(m表示的是目标函数的个数,n是种群的规模).     

一种基于节点能力区分的Pareto最优网络编码组播方法

快速数据分发在突发事件响应,军事领域等具有重要的应用。针对异构用户节点群体下快速数据分发问题,提出基于能力区分的拓扑构建和速率控制的网络编码组播协议CORE。CORE利用能力区分的自适应层次化拓扑构建鼓励节点提供高的上传带宽并优化系统范围吞吐率;利用直方图的方式对基于网络编码的数据传输进行流量控制,降低冗余数据的传输;基于分布式的速率控制实现Pareto最优的下载速率分配。实验结果表明CORE具有良好的可扩展性,能够充分利用异构节点的上传能力,提供区分的下载带宽分配,较高的数据传输吞吐率、低端到端网络延迟,能够提供异构网络环境下分发时间紧迫的数据分发服务。     

Optimal Tax Progressivity in Unionised Labour Markets: Simulation Results for Germany

Changing the income tax progressivity in labour markets with collective wage bargaining generates a trade-off. On the one hand, higher progressivity distorts individual labour supply decisions at the hours-of-work margin, on the other hand, it reduces unemployment by exerting downward pressure on wages. This trade-off is quantitatively assessed using a numerical model for Germany. The model combines a microsimulation module, which captures the labour-supply decisions of approximately 4,600 individual households, and a macro (computable general equilibrium) module, which features collective wage bargaining and involuntary unemployment. In the simulations carried out using this model, the optimal degree of tax progressivity turns out to be higher than the one in the actual German tax schedule. The optimum is located at marginal tax rates that are 6 percentage points higher than the actual rates (combined with a transfer that balances the public budget). The welfare gain from such a reform is modest, however. It amounts to no more than two euros per person per month.     

社会网络中弱关系团队形成问题研究

随着在线社会网络的迅速发展,社会网络的团队形成问题逐渐成为研究热点。现有的社会网络中团队形成问题目标是寻找一个成员间沟通代价最小的团队。然而,实际应用中团队成员间的不紧密关系使得团队的观点多样化、多角度、无偏见,可以广泛应用于形成专家评审团队、大众评审团等。基于此需求,将社会学的弱关系概念引入团队形成问题中,提出了一种社会网络中弱关系团队形成问题。该问题旨在寻找成员间为弱关系,同时满足技能、经验值要求的一个团队,为NP-hard问题。提出了3类算法解决该问题,分别为贪心算法、精确算法、α-近似算法,每类算法有各自的特点与适用范围。利用ACM和DBLP两类真实的数据集进行实验,综合评估了各类算法的效率与求解质量,证明了提出算法的有效性。     

Log-Space Algorithms for Paths and Matchings in k-Trees

Reachability and shortest path problems are NL-complete for general graphs. They are known to be in L for graphs of tree-width 2 (Jakoby and Tantau in Proceedings of FSTTCS’07: The 27th Annual Conference on Foundations of Software Technology and Theoretical Computer Science, pp. 216–227, 2007). In this paper, we improve these bounds for k-trees, where k is a constant. In particular, the main results of our paper are log-space algorithms for reachability in directed k-trees, and for computation of shortest and longest paths in directed acyclic k-trees. Besides the path problems mentioned above, we also consider the problem of deciding whether a k-tree has a perfect matching (decision version), and if so, finding a perfect matching (search version), and prove that these two problems are L-complete. These problems are known to be in P and in RNC for general graphs, and in SPL for planar bipartite graphs, as shown in Datta et al. (Theory Comput. Syst. 47:737–757, 2010). Our results settle the complexity of these problems for the class of k-trees. The results are also applicable for bounded tree-width graphs, when a tree-decomposition is given as input. The technique central to our algorithms is a careful implementation of the divide-and-conquer approach in log-space, along with some ideas from Jakoby and Tantau (Proceedings of FSTTCS’07: The 27th Annual Conference on Foundations of Software Technology and Theoretical Computer Science, pp. 216–227, 2007) and Limaye et al. (Theory Comput. Syst. 46(3):499–522, 2010).     

Maximum Induced Matchings for Chordal Graphs in Linear Time

The Maximum Induced Matching (MIM) Problem asks for a largest set of pairwise vertex-disjoint edges in a graph which are pairwise of distance at least two. It is well-known that the MIM problem is NP-complete even on particular bipartite graphs and on line graphs. On the other hand, it is solvable in polynomial time for various classes of graphs (such as chordal, weakly chordal, interval, circular-arc graphs and others) since the MIM problem on graph G corresponds to the Maximum Independent Set problem on the square G ^*=L(G)² of the line graph L(G) of G, and in some cases, G ^* is in the same graph class; for example, for chordal graphs G, G ^* is chordal. The construction of G ^*, however, requires time, where m is the number of edges in G. Is has been an open problem whether there is a linear-time algorithm for the MIM problem on chordal graphs. We give such an algorithm which is based on perfect elimination order and LexBFS.     

Pareto Effectiveness of Equilibria in Active Systems with the Distributed Control

Active systems with the distributed control (one active element and a few control centers) are considered. Questions for the existence of the Nash equilibrium of the game of centers in pure strategies are studied. It is shown that at any distribution of the incomes from the work of an active system among a few centers and the existence of at least one Pareto-ineffective Nash equilibrium, any Pareto-effective outcome can be implemented as a Nash equilibrium. It is proved that in the system consisting of two centers there always exists a Pareto-effective Nash equilibrium in pure strategies.     

Dominating Induced Matchings for P 7-Free Graphs in Linear Time

Let G be a finite undirected graph with edge set E. An edge set E′?E is an induced matching in G if the pairwise distance of the edges of E′ in G is at least two; E′ is dominating in G if every edge e∈E?E′ intersects some edge in E′. The Dominating Induced Matching Problem (DIM, for short) asks for the existence of an induced matching E′ which is also dominating in G; this problem is also known as the Efficient Edge Domination Problem. The DIM problem is related to parallel resource allocation problems, encoding theory and network routing. It is ${\mathbb{NP}}$ -complete even for very restricted graph classes such as planar bipartite graphs with maximum degree three. However, its complexity was open for P _k -free graphs for any k≥5; P _k denotes a chordless path with k vertices and k?1 edges. We show in this paper that the weighted DIM problem is solvable in linear time for P ₇-free graphs in a robust way.     

Counting the Number of Perfect Matchings in K5-Free Graphs

Counting the number of perfect matchings in graphs is a computationally hard problem. However, in the case of planar graphs, and even for K_3,3-free graphs, the number of perfect matchings can be computed efficiently. The technique to achieve this is to compute a Pfaffian orientation of a graph. In the case of K₅-free graphs, this technique will not work because some K₅-free graphs do not have a Pfaffian orientation. We circumvent this problem and show that the number of perfect matchings in K₅-free graphs can be computed in polynomial time. We also parallelize the sequential algorithm and show that the problem is in TC². We remark that our results generalize to graphs without singly-crossing minor.     

A quantile estimation for massive data with generalized Pareto distribution

This paper proposes a new method of estimating extreme quantiles of heavy-tailed distributions for massive data. The method utilizes the Peak Over Threshold (POT) method with generalized Pareto distribution (GPD) that is commonly used to estimate extreme quantiles and the parameter estimation of GPD using the empirical distribution function (EDF) and nonlinear least squares (NLS). We first estimate the parameters of GPD using EDF and NLS and then, estimate multiple high quantiles for massive data based on observations over a certain threshold value using the conventional POT. The simulation results demonstrate that our parameter estimation method has a smaller Mean square error (MSE) than other common methods when the shape parameter of GPD is at least 0. The estimated quantiles also show the best performance in terms of root MSE (RMSE) and absolute relative bias (ARB) for heavy-tailed distributions.