speye

Sparse identity matrix

Syntax

S = speye

S = speye(n)

S = speye(n,m)

S = speye(sz)

Description

S = speye returns a sparse scalar 1.

S = speye(n) returns a sparse n-by-n identity matrix, with ones on the main diagonal and zeros elsewhere.

S = speye(n,m) returns a sparse n-by-m matrix, with ones on the main diagonal and zeros elsewhere.

S = speye(sz) returns a matrix with ones on the main diagonal and zeros elsewhere. The size vector sz defines size(S). For example, speye([2 3]) returns a 2-by-3 matrix.

Examples

Square Sparse Identity Matrix

Create a 1000-by-1000 square sparse identity matrix and view the sparsity pattern.

I = speye(1000);
spy(I)

The result is the same as sparse(eye(1000)), but this latter version requires temporary storage for the full representation before it is converted to sparse storage.

Rectangular Sparse Identity Matrix

Create a 400-by-800 sparse identity matrix and view the pattern of nonzeros.

S = speye(400,800);
spy(S)

This command is equivalent to speye([400 800]).

Input Arguments

n, m — Dimension sizes

nonnegative integer scalars

Dimension sizes, specified as nonnegative integer scalars.

• If n is the only input argument, then S is an n-by-n identity matrix.

• If n or m is 0, then S is an empty matrix.

• If n or m is negative, then it is treated as 0.

Example: speye(4) creates a 4-by-4 identity matrix.

Example: speye(3,6) creates a 3-by-6 identity matrix.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

sz — Dimension sizes

two-element row vector

Dimension sizes, specified as a two-element row vector. The row vector is of the form [numRows numCols].

• If an element of sz is 0, then S is an empty matrix.

• If an element of sz is negative, then the element is treated as 0.

Example: speye([4 5]) creates a 4-by-5 matrix and is equivalent to speye(4,5).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

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™.

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