Sparse matrices provide efficient storage of double, single, 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.

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 …

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 …

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 available at NACA …

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

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 …

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 …

Types of Eigenvalues west0479 is a real-valued 479-by-479 sparse matrix with both real and complex pairs of conjugate eigenvalues. Load the west0479 matrix, then compute and plot all of the …