Abstract

There are many application problems that emerge in the areas of engineering simulations, scientific computing, information retrieval and economics which use matrixes where non-zero elements are a significant minority with less than 10%. These are universal in many mathematical and scientific applications. These matrixes enable the reduction of storage and computational requirements by storing and carrying out arithmetic with, only the non-zero elements. This is the sparse matrix which must be compressed for these applications. The sparse matrix compression represents non-zero matrix entries. This study presents a novel algorithm for compressing a sparse matrix, which involves three steps. Firstly it involves the division of sparse matrix into sub-matrixes; secondly conducting several transformations; finally coding them. The novel algorithm is called folding. The compressed matrix reduces memory requirement with a good rate compared with the original sparse matrix. C++ is used in the implementation of this algorithm.

Keywords:

Coding, dense matrix, folding, sparse matrix, transformation, unfolding,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.