Sequential algorithm for channel equalisation employing a stack

1976 ◽  
Vol 12 (18) ◽  
pp. 468
Author(s):  
J. Gordon ◽  
N. Montague
Keyword(s):  
Sequential Algorithm

OPTIMAL TREE RANKING IS IN $\mathcal{NC}$

1992 ◽  
Vol 02 (01) ◽  
pp. 31-41 ◽  
Author(s):  
PILAR DE LA TORRE ◽  
RAYMOND GREENLAW ◽  
TERESA M. PRZYTYCKA
Keyword(s):  
General Problem ◽  
Sequential Algorithm ◽  
Natural Numbers ◽  
Ranking Problem ◽  
Crew Pram ◽  
Optimal Tree ◽  
Optimal Ranking

This paper places the optimal tree ranking problem in [Formula: see text]. A ranking is a labeling of the nodes with natural numbers such that if nodes u and v have the same label then there exists another node with a greater label on the path between them. An optimal ranking is a ranking in which the largest label assigned to any node is as small as possible among all rankings. An O(n) sequential algorithm is known. Researchers have speculated that this problem is P-complete. We show that for an n-node tree, one can compute an optimal ranking in O( log n) time using n2/ log n CREW PRAM processors. In fact, our ranking is super critical in that the label assigned to each node is absolutely as small as possible. We achieve these results by showing that a more general problem, which we call the super critical numbering problem, is in [Formula: see text]. No [Formula: see text] algorithm for the super critical tree ranking problem, approximate or otherwise, was previously known; the only known [Formula: see text] algorithm for optimal tree ranking was an approximate one.

A sequential algorithm for the universal coding of finite memory sources

1992 ◽  
Vol 38 (3) ◽  
pp. 1002-1014 ◽  
Author(s):  
M.J. Weinberger ◽  
A. Lempe ◽  
J. Ziv
Keyword(s):  
Sequential Algorithm ◽  
Universal Coding ◽  
Finite Memory

scholarly journals Parallel Strategy to Factorize Fermat Numbers with Implementation in Maple Software

2021 ◽  
pp. 167-173
Author(s):  
Jianhui Li ◽  
◽  
Manlan Liu
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Computational Efficiency ◽  
Sequential Algorithm ◽  
Parallel Processes ◽  
Fermat Numbers ◽  
Fermat Number ◽  
Parallel Strategy

In accordance with the traits of parallel computing, the paper proposes a parallel algorithm to factorize the Fermat numbers through parallelization of a sequential algorithm. The kernel work to parallelize a sequential algorithm is presented by subdividing the computing interval into subintervals that are assigned to the parallel processes to perform the parallel computing. Maple experiments show that the parallelization increases the computational efficiency of factoring the Fermat numbers, especially to the Fermat number with big divisors.

Traffic and Network Performance Monitoring for Effective Quality of Service and Network Management

2010 ◽  
pp. 201-227
Author(s):  
P. Papantoni-Kazakos ◽  
A. T. Burrell
Keyword(s):  
Mobile Networks ◽  
Network Performance ◽  
Performance Monitoring ◽  
Sequential Algorithm ◽  
Heterogeneous Environments ◽  
Time Varying ◽  
Distributed Environment ◽  
Data Flows ◽  
Network Performance Monitoring ◽  
And Performance

The authors consider distributed mobile networks carrying time-varying heterogeneous traffics. To deal effectively with the mobile and time-varying distributed environment, the deployment of traffic and network performance monitoring techniques is necessary for the identification of traffic changes, network failures, and also for the facilitation of protocol adaptations and topological modifications. Concurrently, the heterogeneous traffic environment necessitates the deployment of hybrid information transport techniques. This chapter discusses the design, analysis, and evaluation of distributed and dynamic techniques which manage the traffic and monitor the network performance effectively, while capturing the dynamics inherent in the mobile heterogeneous environments. Specifically, the design of a monitoring sub-network is sought, where the arising research tasks include: • the adoption of a core sequential algorithm which monitors both the variations in the rates of the information data flows and the dynamics of the network performance. • The identification of the specific operational and performance characteristics of the monitoring systems, when the core algorithm is implemented in a distributed environment; for a given network topology, it is important to identify the minimum size monitoring sub-network for complete “visibility” of data flows and network functions. • Dynamically changing monitoring sub-network architectures, as functions of time-varying network topologies. • The deployment of Artificial Intelligence learning techniques, in the presence of dynamically changing network and information flow environments, to appropriately adapt crucial operational parameters of the data monitoring algorithms.

Greedy Algorithms

1995 ◽  
Author(s):  
Raymond Greenlaw ◽  
H. James Hoover ◽  
Walter L. Ruzzo
Keyword(s):  
Greedy Algorithms ◽  
Independent Set ◽  
Independent Sets ◽  
Case Finding ◽  
Sequential Algorithm ◽  
Maximal Independent Set ◽  
Maximum Independent Set ◽  
Greedy Method ◽  
Numerical Order ◽  
Selection Of

We consider the selection of two basketball teams at a neighborhood playground to illustrate the greedy method. Usually the top two players are designated captains. All other players line up while the captains alternate choosing one player at a time. Usually, the players are picked using a greedy strategy. That is, the captains choose the best unclaimed player. The system of selection of choosing the best, most obvious, or most convenient remaining candidate is called the greedy method. Greedy algorithms often lead to easily implemented efficient sequential solutions to problems. Unfortunately, it also seems to be that sequential greedy algorithms frequently lead to solutions that are inherently sequential — the solutions produced by these algorithms cannot be duplicated rapidly in parallel, unless NC equals P. In the following subsections we will examine this phenomenon. We illustrate some of the important aspects of greedy algorithms using one that constructs a maximal independent set in a graph. An independent set is a set of vertices of a graph that are pairwise nonadjacent. A maximum independent set is such a set of largest cardinality. It is well known that finding maximum independent sets is NP-hard. An independent set is maximal if no other vertex can be added while maintaining the independent set property. In contrast to the maximum case, finding maxima? independent sets is very easy. Figure 7.1.1 depicts a simple polynomial time sequential algorithm computing a maximal independent set. The algorithm is a greedy algorithm: it processes the vertices in numerical order, always attempting to add the lowest numbered vertex that has not yet been tried. The sequential algorithm in Figure 7.1.1, having processed vertices 1,... , j -1, can easily decide whether to include vertex j. However, notice that its decision about j potentially depends on its decisions about all earlier vertices — j will be included in the maximal independent set if and only if all j' less than j and adjacent to it were excluded.

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