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 …

This MATLAB function converts a full matrix into sparse form by squeezing out any zero elements.

Sparse Matrix Operations Efficiency of Operations Computational Complexity The computational complexity of sparse operations is proportional to nnz, the number of nonzero elements in the matrix. …

Constructing Sparse Matrices Creating Sparse Matrices MATLAB ® never creates sparse matrices automatically. Instead, you must determine if a matrix contains a large enough percentage of zeros …

Computational Advantages of Sparse Matrices Memory Management Using sparse matrices to store data that contains a large number of zero-valued elements can both save a significant amount of …

Visualize Sparsity Pattern You can use spy to visualize the nonzero elements in a matrix, so it is a particularly useful function to see the sparsity pattern in sparse matrices. spy(A) plots the sparsity …

Use nonzeros to return the nonzero elements in a sparse matrix. Create a 10-by-10 sparse matrix that contains a few nonzero elements. The typical display of sparse matrices shows a list of the nonzero …

Accessing Sparse Matrices Nonzero Elements There are several commands that provide high-level information about the nonzero elements of a sparse matrix:

This MATLAB function factorizes the full or sparse matrix A into an upper triangular matrix U and a permuted lower triangular matrix L such that A = L*U.

This MATLAB function creates a sparse matrix that has the same sparsity pattern as the matrix S, but with normally distributed random entries with mean 0 and variance 1.