Abstract not found

partition obtained after multilevel refinement. We also present a new scheme for refining during uncoarsening that is much faster than the Kernighan-Lin refinement. We test our scheme on a large number of graphs arising in various domains including finite element methods, linear programming, VLSI

We introduce several modifications of the partitioning schemes used in Hoare’s quicksort and quickselect algorithms, including ternary schemes which identify keys less or greater than the pivot. We give estimates for the numbers of swaps made by each scheme. Our computational experiments indicate

In order to support homogeneous distributed object oriented database systems we need to have a clear understanding of what is meant by partitioning a class and what are the different ways of doing so. In this paper, we concentrate on studying the different types of class partitioning schemes

Two closely related energy partitioning schemes, in which the total energy is presented as a sum of atomic and diatomic contributions by using the ‘‘atomic decomposition of identity’’, are compared on the example of N,N-dimethylformamide, a simple but chemically rich molecule. Both schemes account

/O workload distribution becomes important for good performance. Naturally, the performance of a parallel information retrieval system using an inverted file structure is affected by the partitioning scheme of the inverted file. In this paper, we propose two different partitioning schemes for an inverted file

Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc.

Object-oriented database systems are becoming popular and are being used in a large number of application domains. Many of these application domains are inherently distributed. The focus of this work is on articulating the concepts of method induced partitioning schemes in object-oriented databases

into training, validation and test subsets is not appropriate when dealing with agricultural problems characterized with strong spatial data correlation. A simple spatial data partitioning scheme that leads to significantly faster neural network training and slightly better generalization is proposed. Also

of factors such that best approximates the whole set. While this problem has been addressed in the past in a particular case, we present here a general description. Furthermore, we also propose a general partitioning scheme. Our proposal is general in the sense that it is presented in terms of a generic