Hardness and inapproximability of minimizing adaptive distinguishing sequences

An adaptive distinguishing sequence (ADS) can be used for identifying an unknown initial state of a finite state machine (FSM). It has been long known that checking the existence of an ADS for an FSM, and finding an ADS for an FSM when one exists, can be performed in polynomial time. However, the problem of finding a minimum ADS has not been studied so far. Generating a minimum ADS is especially motivated when such an ADS is used repeatedly, e.g. for the construction of a test sequence. We introduce a number of metrics to define a minimum ADS and show that the problem of generating a minimum ADS with respect to these metrics is NP-complete. In addition, we provide inapproximability results for these hard problems and show that not only deciding but also approximating such a minimum ADS is a hard problem. We modify the only polynomial time ADS generation algorithm existing, and experimentally show that these modifications construct reduced ADSs. We also validate the motivation of ADS minimization by presenting experimental results on the effect of using reduced ADSs to generate test sequences.     

An efficient algorithm for constructing delay bounded minimum cost multicast trees

Multimedia applications are usually resource intensive, have stringent quality of service requirements, and in many cases involve large groups of participants. Multicasting is poised to play an important role in future deployment of these applications. This paper focuses on developing delay-bounded, minimum-cost multicast trees, linking a source to a set of multicast destination nodes. The approach taken in this paper is efficient, flexible and unique in the sense that it cleverly limits its computation only to paths that originate at multicast nodes, thereby avoiding computing paths that exclusively link non-multicast nodes. The simulation results show that the multicast trees produced by the proposed heuristic are of lower cost than those produced by other well-known heuristics, including those which use an expensive k-shortest-paths procedure to minimize the cost of the multicast tree. Furthermore, the results show that, in comparison to other heuristics, the proposed scheme results in a significant reduction in the computation time required to build the multicast tree.     

Polynomial-time primal simplex algorithms for the minimum cost network flow problem

We present two variants of the primal network simplex algorithm which solve the minimum cost network flow problem in at mostO(n ² logn) pivots. Here we define the network simplex method as a method which proceeds from basis tree to adjacent basis tree regardless of the change in objective function value; i.e., the objective function is allowed to increase on some iterations. The first method is an extension of theminimum mean augmenting cycle-canceling method of Goldberg and Tarjan. The second method is a combination of a cost-scaling technique and a primal network simplex method for the maximum flow problem. We also show that the diameter of the primal network flow polytope is at mostn ² m.     

Polynomial-time primal simplex algorithms for the minimum cost network flow problem

We present two variants of the primal network simplex algorithm which solve the minimum cost network flow problem in at mostO(n ² logn) pivots. Here we define the network simplex method as a method which proceeds from basis tree to adjacent basis tree regardless of the change in objective function value; i.e., the objective function is allowed to increase on some iterations. The first method is an extension of theminimum mean augmenting cycle-canceling method of Goldberg and Tarjan. The second method is a combination of a cost-scaling technique and a primal network simplex method for the maximum flow problem. We also show that the diameter of the primal network flow polytope is at mostn ² m.This research was supported in part by NSF Grants DMS-85-12277 and CDR-84-21402 and ONR Contract N00014-87-K0214.     

de Bruijn序列查寻表标签的k位修正构造法

de Bruijn序列的结构是一个查寻表,其核心是它的表标签。因此构造出查寻表标签对于生成de Bruijn序列十分重要。给出两种k位修正构造法。方法1为k位提升构造法,即对大部分节点将其第k（k=1,2,…,n-1）位提升一个定值c（1≤c≤m）,来作为该节点的标签。方法2为k位收缩构造法,即对大部分节点将其第k（k=1,2,…,n-1）位向定值r（0≤r≤m）收缩,来作为该节点的标签。这些方法构造的查寻表标签数随着m,n增长而成指数式增长。与定值构造法一样,在局部看是有效的,但与查寻表标签本身数目的惊人增长比较起来就很渺小。方法2与定值标签构造法比较其速度提高了关于m,n的指数式倍。     

高效区分实拍图像与生成图像的方法

提出一种高效的图像区分方法,专注于区分实拍图像与生成图像。该方法在图像预处理过程中,利用了图像尺度及颜色数等几个高效的判断项,将那些容易区分的生成图像召回,减少了后期垃圾图像与文字图像判断项的判断量,减少了图像筛选运行时间。     

Computational comparison of several greedy algorithms for the minimum cost perfect matching problem on large graphs

The aim of this paper is to computationally compare several algorithms for the Minimum Cost Perfect Matching Problem on an undirected complete graph. Our work is motivated by the need to solve large instances of the Capacitated Arc Routing Problem (CARP) arising in the optimization of garbage collection in Denmark. Common heuristics for the CARP involve the optimal matching of the odd-degree nodes of a graph. The algorithms used in the comparison include the CPLEX solution of an exact formulation, the LEDA matching algorithm, a recent implementation of the Blossom algorithm, as well as six constructive heuristics. Our results show that two of the constructive heuristics consistently exhibit the best behavior compared with the other four.     

Two algorithms for constructing a Delaunay triangulation

This paper provides a unified discussion of the Delaunay triangulation. Its geometric properties are reviewed and several applications are discussed. Two algorithms are presented for constructing the triangulation over a planar set ofN points. The first algorithm uses a divide-and-conquer approach. It runs inO(N logN) time, which is asymptotically optimal. The second algorithm is iterative and requiresO(N ²) time in the worst case. However, its average case performance is comparable to that of the first algorithm.This work was supported in part by the National Science Foundation under grant MCS-76-17321 and the Joint Services Electronics Program under contract DAAB-07-72-0259.     

Effective algorithms for circle fitting

After an investigation of known methods for circle fitting two new effective algorithms are proposed which meet all the requirements of mass data handling.     

A methodology for constructing fuzzy algorithms for learning vectorquantization

This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algorithms can be accomplished by selecting the form of the interference function that determines the effect of the nonwinning prototypes on the attraction between the winning prototype and the input of the network. The proposed methodology provides the basis for extending the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms. This paper also introduces two quantitative measures which establish a relationship between the formulation that led to FALVQ algorithms and the competition between the prototypes during the learning process. The proposed algorithms and competition measures are tested and evaluated using the IRIS data set. The significance of the proposed competition measure is illustrated using FALVQ algorithms to perform segmentation of magnetic resonance images of the brain.     

Efficient parallel branch-and-bound algorithm for constructing minimum ultrametric trees

The construction of evolutionary trees is important for computational biology, especially for the development of biological taxonomies. The ultrametric tree (UT) is a commonly used model for evolutionary trees assuming that the rate of evolution is constant (molecular clock hypothesis). However, the construction of minimum ultrametric trees (MUTs, principle of minimum evolution) has been shown to be NP-hard even from a metric distance matrix. The branch-and-bound algorithm is generally used to solve a wide variety of NP-hard problems. In previous work, a sequential branch-and-bound algorithm for constructing MUTs (BBU) was presented and the experimental results showed that it is useful for MUT construction. Hence, in this study, an efficient parallel branch-and-bound algorithm (PBBU) for constructing MUTs or near-MUTs from a metric distance matrix was designed. A random data set as well as some practical data sets of Human + Chimpanzee Mitochondrial and Bacteriophage T7 DNAs were used to test the PBBU. The experimental results show that the PBBU found an optimal solution for 36 species on 16 PCs within a reasonable time. To the best of our knowledge, no algorithm has been found to solve this problem even for 25 species. Moreover, the PBBU achieved satisfying speed-up ratios for most of the test cases.     

最小耗费生成树剔除算法及其正确性证明

文章提出了一种新的最小耗费生成树的算法，并对其正确性进行了证明。该算法通过从原图中逐步别除边来形成生成树，特别适用于当原图中边数较少(相对于顶点数)，或原图规模不大的情形。     

The relation between preset distinguishing sequences and synchronizing sequences

We study the relation between synchronizing sequences and preset distinguishing sequences which are some special sequences used in finite state machine based testing. We show that the problems related to preset distinguishing sequences can be converted into related problems of synchronizing sequences. Using the results existing in the literature for synchronizing sequences, we offer several reflections of these results for preset distinguishing sequences. Although computing a preset distinguishing sequence is PSPACE-hard , we do identify a class of machines for which computing a preset distinguishing sequence can be performed in polynomial time and argue that this class is practically relevant. We also present an experimental study to compare the performance of exponential brute-force and polynomial heuristic algorithms to compute a preset distinguishing sequence.     

On universal algorithms for adaptive forecasting

In the last decade, new methods of forecasting were developed different from traditional statistical methods. In particular, it is possible to “efficiently” predict any sequence of outcomes without using any hypothesis on the nature of a source generating it. In the present paper, a modified version of the universal forecasting algorithm is considered. The main part of the paper is devoted to algorithmic analysis of universal forecasting methods and to exploring limits of their performance.     

Semantics-based inference algorithms for adaptive visualenvironments

The paper presents a grammatical inference methodology for the generation of visual languages, that benefits from the availability of semantic information about the sample sentences. Several well-known syntactic inference algorithms are shown to obey a general inference scheme, which the authors call the Gen-Inf scheme. Then, all the algorithms of the Gen-Inf scheme are modified in agreement with the introduced semantics-based inference methodology. The use of grammatical inference techniques in the design of adaptive user interfaces was previously experimented with the VLG system for visual language generation. The system is a powerful tool for specifying, designing, and interpreting customized visual languages for different applications. They enhance the adaptivity of the VLG system to any visual environment by exploiting the proposed semantics-based inference methodology. As a matter of fact, a more general model of visual language generation is achieved, based on the Gen-Inf scheme, where the end-user is allowed to choose the algorithm which best fits his/her requirements within the particular application environment     

Several algorithms for finite-model adaptive control

Finite-model adaptive control problem is studied for a class of discrete-time nonlinear uncertain systems. This problem was motivated by recent efforts on the capability and limitation of feedback mechanism and has the characteristics of “essentially” finite internal uncertainties. To solve this type of problem, based on different ideas, we introduce several approaches, controller falsification, controller combination, and pseudo-parameter estimation, to design the feedback control law and rigorously establish the stability of closed-loop system for several typical algorithms in these approaches. Our results show that, under reasonably weak conditions, capable feedback control laws exist dealing with the finite internal uncertainties of the system. These results together with related results in companion papers provide partial answers to the finite-model adaptive control problem and may lead to deeper understanding on the capability of the whole feedback mechanism.