spconvert

Import from sparse matrix external format

Syntax

S = spconvert(D)

Description

S = spconvert(D) constructs sparse matrix S from the columns of D in a manner similar to the sparse function.

• If D is of size N-by-3, then spconvert uses the columns [i,j,re] of D to construct S, such that S(i(k), j(k)) = re(k).

• If D is of size N-by-4, then spconvert uses the columns [i,j,re,im] of D to construct S, such that S(i(k), j(k)) = re(k) + 1i*im(k).

Examples

Convert Data File to Sparse Matrix

Create an ASCII file, uphill.dat, which contains the following values. Save the file in your current directory.

1    1    1.000000000000000
1    2    0.500000000000000
2    2    0.333333333333333
1    3    0.333333333333333
2    3    0.250000000000000
3    3    0.200000000000000
1    4    0.250000000000000
2    4    0.200000000000000
3    4    0.166666666666667
4    4    0.142857142857143
4    4    0.000000000000000

It is common to purposefully make the last line of the file include the desired size of the matrix with a value of 0. This practice ensures that the converted sparse matrix has that size.

Load the data into MATLAB^® and convert it into a sparse matrix.

load uphill.dat
H = spconvert(uphill)

H =

  4×4 sparse double matrix (10 nonzeros)

   (1,1)       1.0000
   (1,2)       0.5000
   (2,2)       0.3333
   (1,3)       0.3333
   (2,3)       0.2500
   (3,3)       0.2000
   (1,4)       0.2500
   (2,4)       0.2000
   (3,4)       0.1667
   (4,4)       0.1429

In this case, the last line in the file is not necessary because the earlier lines already specify that the matrix is at least 4-by-4.

Input Arguments

D — Input matrix

matrix

Input matrix, specified as a matrix with either three or four columns. In both cases, the first two columns of D are subscripts and the third column is composed of data values. A four column matrix specifies the real (third column) and imaginary (fourth column) parts of complex numbers.

If D is already a sparse matrix, then spconvert returns D.

Data Types: single | double

Extended Capabilities

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 spconvert 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