SOLVING MINIMUM SPANNING TREE PROBLEM WITH DNA COMPUTING

Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.

Brain Functional Network Based on Small-Worldness and Minimum Spanning Tree for Depression Analysis

Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization.

Salience adaptive morphological structuring element construction method based on minimum spanning tree

Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,its shape may be changed and part of the information may be lost.Therefore,we propose a method for constructing salience adaptive morphological structuring elements based on minimum spanning tree(MST).First,the gradient image of the input image is calculated,the edge image is obtained by non-maximum suppression(NMS)of the gradient image,and then chamfer distance transformation is performed on the edge image to obtain a salience map(SM).Second,the radius of structuring element is determined by calculating the maximum and minimum values of SM and then the minimum spanning tree is calculated on the SM.Finally,the radius is used to construct a structuring element whose shape and size adaptively change with the local features of the input image.In addition,the basic morphological operators such as erosion,dilation,opening and closing are redefined using the adaptive structuring elements and then compared with the classical morphological operators.The simulation results show that the proposed method can make full use of the local features of the image and has better processing results in image structure preservation and image filtering.

On the Minimum Spanning Tree Determined by n Points in the Unit Square

Let P n be a set of n points in the unit square S,l(P n) denoe the length of the minimum spanning tree of P n, andC n= max P nSl(P n), n=2,3,… In this paper,the exact value of C n for n=2,3,4 and the corresponding configurations are given. Additionally,the conjectures of the configuration for n=5,6,7,8,9 are proposed.

The Design of the Minimum Spanning Tree Algorithms

Based on the graphic theory and improved genetic algorithm,an improved genetic algorithm to search the minimum spanning trees is given . The algorithm uses binary code to represent the problem of minimum spanning trees. It designs the corresponding fitness function,operator and few controlling strategies to improve its speed and evolutionary efficiency.Only one solution can be gotten with running traditional al-gorithem atone time.The new algorithm can get a set of the solutions with higher probability in a shorter time.The experiment shows that it has a better performance than traditional methods.

MEAN-VARIANCE MODEL BASED ON FILTERS OF MINIMUM SPANNING TREE

This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data of the 30 stocks of the Hang Seng Index （HSI） and Dow Jones Index （DJI） from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that （1） although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and （2） the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.

ON THE INVERSE MINIMUM SPANNING TREE PROBLEM WITH MINIMUM NUMBER OF PERTURBED EDGES

Let G=<V, E, L> be a network with the vertex set V, the edge set E and the length vector L, and let T* be a prior determined spanning tree of G. The inverse minimum spanning tree problem with minimum number of perturbed edges is to perturb the length vector L to L+ , such that T* is one of minimum spanning trees under the length vector L+ and the number of perturbed edges is minimum. This paper establishes a mathematical model for this problem and transforms it into a minimum vertex covering problem in a bipartite graph G0, a path-graph. Thus a strongly polynomial algorithm with time complexity O(mn2) can be designed by using Hungarian method.

Efficient Minimum Spanning Tree Algorithms on the Reconfigurable Mesh

The reconfigurable mesh consists of an array of processors interconnected by a reconfigurable bus system. The bus system can be used to dynamically obtain various interconnection patterns among the processors. Recently, this model has attracted a lot of attention. In this paper, two efficient algorithms are proposed for computing the minimum spanning tree of an n-vertex undirected graph. One runs on an n×n reconfigurable mesh with time complexity of O(log^2 n). The other runs with time complexity of O(log n) on an n^(1+E)×n reconfigurable mesh, where < E < 1 is a constant. All these improve the previously known results on the reconfigurable mesh.

Investigating the Disparities of China's Insurance Market Based on Minimum Spanning Tree from the Viewpoint of Geography and Enterprise

In this paper, we investigate the disparities of China’s insurance market from the viewpoint of geography and enterprise by using the monthly data from January 2006 to December 2015. We divide the whole insurance market into two parts, namely property insurance and personal insurance.By constructing and analyzing minimum spanning trees of insurance market, we obtain the results as follows:(i) The connections between provinces are much closer than those of firms, and there are regional links between neighboring provinces in the minimum spanning tree(MST); and(ii) the domestic funded firms and foreign funded firms form two explicit clusters in the MSTs of property and personal insurance market.

Minimum Spanning Trees Across Well-Connected Cities and with Location-Dependent Weights

Consider n nodes{X_(i)}_(1≤i≤n) independently and identically distributed(i.i.d.)across N cities located within the unit square S.Each city is modelled as an r_(n)×r_(n)square,and MSTC_(n)denotes the weighted length of the minimum spanning tree containing all the n nodes,where the edge length between nodes X_(i)and X_(j)is weighted by a factor that depends on the individual locations of X_(i)and X_(j).We use approximation methods to obtain variance estimates for MSTC_(n)and prove that if the cities are well connected in a certain sense,then MSTC_(n)appropriately centred and scaled converges to zero in probability.Using the above proof techniques we also study MST_(n),the length of the minimum weighted spanning tree for nodes distributed throughout the unit square S with location-dependent edge weights.In this case,the variance of MST_(n)grows at most as a power of the logarithm of n and we use a subsequence argument to get almost sure convergence of MST_(n),appropriately centred and scaled.

Finding Cut-Edges and the Minimum Spanning Tree via Semi-Tensor Product Approach

Using the semi-tensor product of matrices, this paper investigates cycles of graphs with application to cut-edges and the minimum spanning tree, and presents a number of new results and algorithms. Firstly, by defining a characteristic logical vector and using the matrix expression of logical functions, an algebraic description is obtained for cycles of graph, based on which a new necessary and sufficient condition is established to find all cycles for any graph. Secondly, using the necessary and sufficient condition of cycles, two algorithms are established to find all cut-edges and the minimum spanning tree, respectively. Finally, the study of an illustrative example shows that the results/algorithms presented in this paper are effective.

MINIMUM CONGESTION SPANNING TREES IN BIPARTITE AND RANDOM GRAPHS

The first problem considered in this article reads： is it possible to find upper estimates for the spanning tree congestion in bipartite graphs, which are better than those for general graphs？ It is proved that there exists a bipartite version of the known graph with spanning tree congestion of order n3/2, where n is the number of vertices. The second problem is to estimate spanning tree congestion of random graphs. It is proved that the standard model of random graphs cannot be used to find graphs whose spanning tree congestion has order greater than n3/2.

THE DESIGN AND ANALYSIS OF ALGORITHM OF MINIMUM COST SPANNING TREE

This paper provides a method of producing a minimum cost spanning tree (MCST) using set operations. It studies the data structure for implementation of set operations and the algorithm to be applied to this structure and proves the correctness and the complexity of the algorithm. This algorithm uses the FDG (formula to divide elements into groups) to sort (the FDG sorts a sequence of n elements in expected tir O(n)) and uses the method of path compression to find and to unite. Therefore. n produces an MCST of an undirected network having n vertices and e edges in expected time O(eG(n)).

A Novel Binary Firefly Algorithm for the Minimum Labeling Spanning Tree Problem

Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.

基于群论的频率图在旅行商问题中的应用

针对最小生成树(minimum spanning tree,MST)和旅行商问题(travelling salesman problem,TSP),介绍了完全图上的两类特殊图并定义了这些图上的交运算,每类特殊图和交运算构成一个半群。根据半群性质计算出频率图,分析了最优哈密顿圈(optimal Hamiltonian cycle,OHC)和MST中边的频率性质,证明了频率图上OHC中边的频率下界,该频率下界用于缩小OHC的搜索空间,降低了TSP的求解难度。此外,采用一些TSP算例验证了频率图上OHC中边的频率性质。

Exact Algorithm to Solve the Minimum Cost Multi-Constrained Multicast Routing Problem

The optimal solution of the multi-constrained QoS multicast routing problem is a tree-like hierarchical structure in the topology graph. This multicast route contains a feasible path from the source node to each of the destinations with respect to a set of QoS constraints while minimizing a cost function. Often, it is a tree. In other cases, the hierarchies can return several times to nodes and links of the topology graph. Similarly to Steiner problem, finding such a structure is an NP-hard problem. The usual tree and topology enumeration algorithms applied for the Steiner problem cannot be used to solve the addressed problem. In this paper, we propose an exact algorithm based on the Branch and Bound principle and improved by the Lookahead technique. We show relevant properties of the optimum hierarchy permitting efficient pruning of the search space. To our knowledge, our paper is the first to propose an exact algorithm for this non-trivial multi-constrained optimal multicast route computation. Simulations illustrate the efficiency of the proposed pruning operations. The analysis of the execution time shows that in simple topologies and with tight QoS constraints the exact algorithm requires relatively little execution time. With loose constraints the computation time cannot be tolerated even for off-line route computation. In these cases, the solution is close to a Steiner tree and heuristics can be applied. These results can serve as basis for the design of efficient, polynomial-time routing algorithms.

最小生成树的prim算法及minimum函数

本文介绍了最小生成树的prim算法,minimum函数的实现过程及该函数对由prim算法所得到的最小生成树的影响。

高动态协同拓扑在线优化与分布式制导方法

针对高速飞行器集群飞行过程中的分布式协同制导问题,提出了基于最小生成树的自适应拓扑优化与制导方法。构建了飞行器集群通信链路的拓扑图,根据通信代价将距离量化为权值,通过最小生成树中Kruskal算法思想实时生成最优通信拓扑图;采用分布式双层协同制导构架与多中心分布式拓扑优化决策方法,在一致性协同控制律中对协调变量进行补偿计算;实现了飞行器集群协同飞行中的拓扑自适应优化调控。仿真验证结果表明了所提方法的有效性与优异性能。

基于最小Spanning树的中继节点部署算法

在能量受限的无线传感器网络(WSNs)中,部署中继节点成为确保通信覆盖的常用方法。提出基于最小Spanning树的中继节点部署算法(MST-DRN)。MST-DRN算法引用基于无线充电的能量模型。再依据节点的能量采集率计算边权值。依据边权值的图模型构建最小Spanning树(MST)。检测MST中不满足生存条件的非支叶节点,在节点附近部署中继节点,维持网络连通。实验数据表明:提出的MST-DRN算法在确保网络连通时,减少了成本(中继节点数),并提高了数据包传递率。

原油价格不确定性测度及其对宏观经济的非对称影响研究

有效测度原油价格不确定性并研究其对我国宏观经济的影响,具有重要的理论和现实意义。本文基于最小生成树模型,从全局视角衡量原油多元定价机制中195个影响因素的相对重要程度,并采用高维因子非预期条件波动率模型测度国际原油价格的不确定性。基于收敛交叉映射模型,本文对原油价格不确定性与宏观经济间的非线性因果关系进行识别,研究发现仅存在原油价格不确定性对固定资产投资、产出增速的单向因果关系。进一步地,在厘清原油价格不确定性对宏观经济影响机制的基础上,利用包含马尔可夫区制转移特征的向量自回归模型考察原油价格不确定性对我国宏观经济的影响,理论与实证结果均表明原油价格不确定性对我国宏观经济具有显著负向影响;在不同经济状态下,原油价格不确定性对投资、产出增速的影响具有非对称性。本研究为有效防范原油市场风险、保障能源安全和推动经济稳定增长提供了理论支持。