MATLAB Programming

1 Data Structures
Full and Sparse Matrices
It is not uncommon to have matrices with a large number of zero-valued
elements and, because MATLAB stores zeros in the same way it stores any
other numeric value, these elements can use memory space unnecessarily and
can sometimes require extra computing time.
Sparse matrices provide a way to store data that has a large percentage of
zero elements more efficiently. While full matrices internally store every
element in memory regardless of value, sparse matrices store only the nonzero
elements and their row indices. Using sparse matrices can significantly
reduce the amount of memory required for data storage.
You can create sparse matrices for the double and logical data types. All
MATLAB built-in arithmetic, logical, and indexing operations can be applied
to sparse matrices, or to mixtures of sparse and full matrices. Operations
on sparse matrices return sparse matrices and operations on full matrices
return full matrices.
See the section on Sparse Matrices in the MATLAB Mathematics
documentation for more information onworkingwithsparsematrices.
Sparse Matrix Functions
Thistableshowssomeofthefunctions most commonly used when working
with sparse matrices.
Function
full
issparse
nnz
nonzeros
nzmax
spalloc
sparse
Description
Convert a sparse matrix to a full matrix.
Determine if a matrix is sparse.
Return the number of nonzero matrix elements.
Return the nonzero elements of a matrix.
Return the amount of storage allocated for nonzero
elements.
Allocate spaceforasparsematrix.
Create a sparse matrix or convert full to sparse.
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