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Elementary sparse matrices, reordering algorithms,
iterative methods, sparse linear algebra
Sparse matrices provide efficient storage of double or logical data
that has a large percentage of zeros. While full (or dense)
matrices store every single element in memory regardless of value, sparse matrices
store only the nonzero elements and their row indices. For this reason,
using sparse matrices can significantly reduce the amount of memory
required for data storage.
All MATLAB® built-in arithmetic, logical, and indexing operations
can be applied to sparse matrices, or to mixtures of sparse and full
matrices. Operations on sparse matrices return sparse matrices and
operations on full matrices return full matrices. For more information,
see Computational Advantages of Sparse Matrices and Constructing Sparse Matrices.