Title
Optimised sparse storage mode for symbolic analysis of large networks
Abstract
Symbolic network analysis gained growing interest as it aims at producing outputs in the form of expressions that containing both variables and numbers. However, such analysis faces the primary difficulty of the exponential growth of product terms in a symbolic network function with respect to circuit size. This long-standing difficulty is only partially overcome by various symbolic approximations and hierarchical decomposition approaches. A new storage scheme called Row-Indexed Semi-Symmetric Sparse (RISS) storage mode that partially solves this difficulty is presented in this paper. Unlike other similar storage schemes, the proposed scheme requires only about twice the number of nonzero matrix elements at most. The efficiency of the proposed RISS storage mode is assessed by considering several matrices of moderate sizes and comparing the memory requirement for each matrix in full storage mode and in RISS storage mode. The overall performance of a solver that incorporates the RISS storage mode and the sparse matrix techniques is assessed by considering a typical example of a 90° phase splitting network. When compared to an alternative matrix solver based on successive matrix reduction, the proposed solver demonstrates a reduction of 65% in the operation count and a reduction of 60% in the average memory storage requirement.
Year
DOI
Venue
2007
10.1016/j.advengsoft.2006.07.002
Advances in Engineering Software
Keywords
Field
DocType
Symbolic analysis,Optimised sparse storage mode,Automatic code generation,Analysis of large networks
Mathematical optimization,Skyline matrix,Expression (mathematics),Computer science,Matrix (mathematics),Algorithm,Theoretical computer science,Symbolic data analysis,Network analysis,Solver,Sparse matrix,Exponential growth
Journal
Volume
Issue
ISSN
38
2
0965-9978
Citations 
PageRank 
References 
0
0.34
1
Authors
3
Name
Order
Citations
PageRank
M.A. Al-Taee100.34
Fawzi M. Al-Naima2173.79
B.Z. Al-Jewad300.34