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Data structures in Java for matrix computations
Author(s) -
Gundersen Geir,
Steihaug Trond
Publication year - 2004
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.793
Subject(s) - java , computer science , computation , sparse matrix , flexibility (engineering) , matrix (chemical analysis) , data structure , parallel computing , computational science , algorithm , flexibility method , programming language , mathematics , physics , materials science , quantum mechanics , composite material , gaussian , statistics , finite element method , thermodynamics
In this paper we show how to utilize Java's native arrays for matrix computations. The disadvantages of Java arrays used as a 2D array for dense matrix computation are discussed and ways to improve the performance are examined. We show how to create efficient dynamic data structures for sparse matrix computations using Java's native arrays. This data structure is unique for Java and shown to be more dynamic and efficient than the traditional storage schemes for large sparse matrices. Numerical testing indicates that this new data structure, called Java Sparse Array, is competitive with the traditional Compressed Row Storage scheme on matrix computation routines. Java gives increased flexibility without losing efficiency. Compared with other object‐oriented data structures Java Sparse Array is shown to have the same flexibility. Copyright © 2004 John Wiley & Sons, Ltd.