spfun

Apply function to nonzero sparse matrix elements

Syntax

f = spfun(fun,S)


Description

The spfun function selectively applies a function to only the nonzero elements of a sparse matrix S, preserving the sparsity pattern of the original matrix (except for underflow or if fun returns zero for some nonzero elements of S).

f = spfun(fun,S) evaluates fun(S) on the elements of S that are nonzero. fun is a function handle.

Parameterizing Functions explains how to provide additional parameters to the function fun, if necessary.

Examples

Given the 4-by-4 sparse diagonal matrix

S = spdiags([1:4]',0,4,4)

S = 
   (1,1)        1
   (2,2)        2
   (3,3)        3
   (4,4)        4

Because fun returns nonzero values for all nonzero element of S, f = spfun(@exp,S) has the same sparsity pattern as S.

f =
   (1,1)       2.7183
   (2,2)       7.3891
   (3,3)      20.0855
   (4,4)      54.5982

whereas exp(S) has 1s where S has 0s.

full(exp(S))

ans =
    2.7183    1.0000    1.0000    1.0000
    1.0000    7.3891    1.0000    1.0000
    1.0000    1.0000   20.0855    1.0000
    1.0000    1.0000    1.0000   54.5982

Tips

Functions that operate element-by-element, like those in the elfun directory, are the most appropriate functions to use with spfun.

Extended Capabilities

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

GPU Arrays

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

This function fully supports GPU arrays. 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™.

Usage notes and limitations:

• On each lab, fun receives as input only the nonzero elements of S that are local to the lab. Therefore, functions that operate element by element are the most appropriate for use with spfun.

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).