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 …

This MATLAB function creates a sparse matrix that has the same sparsity pattern as the matrix S, but with uniformly distributed random entries.

This MATLAB function creates an all-zero sparse matrix S of size m-by-n with room to hold nz nonzero elements, where nz >= 1.

Rectangular Sparse Identity Matrix Create a 400-by-800 sparse identity matrix and view the pattern of nonzeros.

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 …

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.

Cholesky Factor of Sparse Matrix Calculate the Cholesky factor of a sparse matrix, and use the permutation output to create a Cholesky factor with fewer nonzeros. Create a sparse positive definite …