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, …

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

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

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 …

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 …

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 …

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

Graphical Representation of Sparse Matrices This example shows the finite element mesh for a NASA airfoil, including two trailing flaps. More information about the history of airfoils is …

Adjacency Matrix of Graph Create a directed graph using an edge list, and then find the equivalent adjacency matrix representation of the graph. The adjacency matrix is returned as a …

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.