Using the C Math Library

Sparse Matrices

The MATLAB C Math Library includes routines to create and manipulate sparse arrays. Sparse matrices provides a more efficient storage format for two-dimensional numeric arrays with few non-zero elements. Only two-dimensional numeric arrays can be converted to sparse storage format.

This table lists the MATLAB C Math Library routines used to create sparse matrices and perform some basic operations on them. The sections that follow provide more detail about using these routines. For more detailed information about using sparse arrays, see Using MATLAB. For more detailed information about any of the library routines, see the online MATLAB C Math Library Reference.

Table 3-2: Sparse Matrix Routines 

To ...	Use ...
Create a sparse matrix	mlfSparse()
Convert a sparse matrix into a full matrix	mlfFull()
Replace nonzero sparse matrix elements with ones	mlfSpones()
Replace nonzero sparse matrix elements with random numbers	mlfSprand() mlfSprandn() mlfSprandnsym()
Import from external sparse matrix format	mlfSpconvert()
Create a sparse identity matrix	mlfSpeye()
Extract a band or diagonal group of elements from a matrix and create a sparse matrix	mlfSpdiags()
Determine the number of nonzero elements in a numeric matrix.	mlfNnz()
Determine if a matrix has any nonzero elements or if all elements are nonzero	mlfAny() or mlfAll()
Determine the amount of storage allocated for the nonzero elements of a sparse matrix	mlfNzmax()
Apply a function to all the nonzero elements of a sparse matrix	mlfSpfun()

Example Program: Creating Numeric Arrays (ex1.c)

Creating a Sparse Matrix