full

Convert sparse matrix to full storage

Syntax

A = full(S)

Description

A = full(S) converts sparse matrix S to full storage organization, such that issparse(A) returns logical 0 (false).

Examples

Convert Sparse Matrix to Full Storage

Change the storage format of a matrix and compare the storage requirements.

Create a random sparse matrix. The display of sparse matrices in MATLAB® omits all zeros and shows the location and value of nonzero elements.

rng default %for reproducibility
S = sprand(8,8,0.3)

S = 8×8 sparse double matrix (15 nonzeros)
   (2,1)       0.0344
   (7,1)       0.4456
   (8,1)       0.7547
   (2,2)       0.4387
   (4,3)       0.7655
   (7,3)       0.6463
   (8,4)       0.2760
   (1,6)       0.9502
   (5,6)       0.1869
   (8,6)       0.6797
   (3,7)       0.3816
   (4,7)       0.7952
   (8,7)       0.6551
   (6,8)       0.4898
   (7,8)       0.7094

Convert the matrix to full storage. The MATLAB display of the matrix reflects the new storage format.

A = full(S)

A = 8×8

         0         0         0         0         0    0.9502         0         0
    0.0344    0.4387         0         0         0         0         0         0
         0         0         0         0         0         0    0.3816         0
         0         0    0.7655         0         0         0    0.7952         0
         0         0         0         0         0    0.1869         0         0
         0         0         0         0         0         0         0    0.4898
    0.4456         0    0.6463         0         0         0         0    0.7094
    0.7547         0         0    0.2760         0    0.6797    0.6551         0

Compare the storage requirements of the two formats:

• A uses storage for 64 doubles (8 bytes each), or $64\cdot 8=512$ bytes.

• S uses storage for 15 nonzero elements, as well as 24 integers describing their positions, for a total of $39\cdot 8=312$ bytes.

whos

  Name      Size            Bytes  Class     Attributes

  A         8x8               512  double              
  S         8x8               312  double    sparse    

Input Arguments

S — Sparse matrix to convert

matrix

Sparse matrix to convert, specified as a matrix. If S is already a full matrix, then A is identical to S.

Tips

• If X is an m-by-n matrix with nz nonzero elements, then full(X) requires space to store m*n elements. On the other hand, sparse(X) requires space to store nz elements and (nz+n+1) integers.

  The density of a matrix (nnz(X)/numel(X)) determines whether it is more efficient to store the matrix as sparse or full. The exact crossover point depends on the matrix class, as well as the platform. For example, in 32-bit MATLAB^®, a double sparse matrix with less than about 2/3 density requires less space than the same matrix in full storage. In 64-bit MATLAB, however, double matrices with fewer than half of their elements nonzero are more efficient to store as sparse matrices.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The full function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a