We recently introduced a new method for Sparse matrix storage[1] which will considerably reduce the storage space by storing only nonzero elements along with the weight of each row(or column) and the number of rows(or column). This paper discusses two algorithms, SMDC Algorithm to convert a sparse matrix into decimal coding format and Reverse SMDC Algorithm to convert a decimally coded matrix back into the normal sparse matrix format. SMDC is a space optimized storage method for storing sparse matrices. It can store a sparse matrix with m rows and n columns and nnz nonzero elements, with smaller (m or n) + nnz +1 storage space, which is very much space efficient storage compared to most of the sparse matrix storage methods.

Sparse Matrices are having very important role in computation, especially managing linear algebraic systems. Its used from storage to computation for different applications like solving partial differential equations, computer graphics, Machine learning, maps and graphs etc. Heavy usage of sparse matrix for different computational applications in a wide variety of areas make optimization of its storage very important. There are several models developed for representing sparse matrices in memory efficiently and many researchers are working on it. here, We are proposing a new method for storing sparse matrices efficiently by using very less storage space.