zeros

Create array of all zeros

Syntax

X = zeros

X = zeros(n)

X = zeros(sz1,...,szN)

X = zeros(sz)

X = zeros(___,typename)

X = zeros(___,'like',p)

Description

X = zeros returns the scalar 0.

X = zeros(n) returns an n-by-n matrix of zeros.

X = zeros(sz1,...,szN) returns an sz1-by-...-by-szN array of zeros where sz1,...,szN indicate the size of each dimension. For example, zeros(2,3) returns a 2-by-3 matrix.

X = zeros(sz) returns an array of zeros where size vector sz defines size(X). For example, zeros([2 3]) returns a 2-by-3 matrix.

X = zeros(___,typename) returns an array of zeros of data type typename. For example, zeros('int8') returns a scalar, 8-bit integer 0. You can use any of the input arguments in the previous syntaxes.

X = zeros(___,'like',p) returns an array of zeros like p; that is, of the same data type (class), sparsity, and complexity (real or complex) as p. You can specify typename or 'like', but not both.

Examples

Matrix of Zeros

Create a 4-by-4 matrix of zeros.

X = zeros(4)

X = 4×4

     0     0     0     0
     0     0     0     0
     0     0     0     0
     0     0     0     0

3-D Array of Zeros

Create a 2-by-3-by-4 array of zeros.

X = zeros(2,3,4);
size(X)

ans = 1×3

     2     3     4

Clone Size from Existing Array

Create an array of zeros that is the same size as an existing array.

A = [1 4; 2 5; 3 6];
sz = size(A);
X = zeros(sz)

X = 3×2

     0     0
     0     0
     0     0

It is a common pattern to combine the previous two lines of code into a single line:

X = zeros(size(A));

Specify Data Type of Zeros

Create a 1-by-3 vector of zeros whose elements are 32-bit unsigned integers.

X = zeros(1,3,'uint32')

X = 1x3 uint32 row vector

   0   0   0

class(X)

ans = 
'uint32'

Clone Complexity from Existing Array

Create a scalar 0 that is complex like an existing array instead of real valued.

First, create a complex vector.

p = [1+2i 3i];

Create a scalar 0 that is complex like p.

X = zeros('like',p)

X = 
0.0000 + 0.0000i

Clone Sparsity from Existing Array

Create a 10-by-10 sparse matrix.

p = sparse(10,10,pi);

Create a 2-by-3 matrix of zeros that is sparse like p.

X = zeros(2,3,'like',p)

X = 2x3 sparse double matrix
   All zero

Clone Size and Data Type from Existing Array

Create a 2-by-3 array of 8-bit unsigned integers.

p = uint8([1 3 5; 2 4 6]);

Create an array of zeros that is the same size and data type as p.

X = zeros(size(p),'like',p)

X = 2x3 uint8 matrix

   0   0   0
   0   0   0

class(X)

ans = 
'uint8'

Clone Distributed Array

If you have Parallel Computing Toolbox™, create a 1000-by-1000 distributed array of zeros with underlying data type int8. For the distributed data type, the 'like' syntax clones the underlying data type in addition to the primary data type.

p = zeros(1000,'int8','distributed');

Starting parallel pool (parpool) using the 'local' profile ...
connected to 6 workers.

Create an array of zeros that is the same size, primary data type, and underlying data type as p.

X = zeros(size(p),'like',p);

class(X)

ans =

    'distributed'

underlyingType(X)

ans =

    'int8'

Input Arguments

n — Size of square matrix

integer value

Size of square matrix, specified as an integer value.

• If n is 0, then X is an empty matrix.

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

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

sz1,...,szN — Size of each dimension (as separate arguments)

integer values

Size of each dimension, specified as separate arguments of integer values.

• If the size of any dimension is 0, then X is an empty array.

• If the size of any dimension is negative, then it is treated as 0.

• Beyond the second dimension, zeros ignores trailing dimensions with a size of 1. For example, zeros(3,1,1,1) produces a 3-by-1 vector of zeros.

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

sz — Size of each dimension (as a row vector)

integer values

Size of each dimension, specified as a row vector of integer values. Each element of this vector indicates the size of the corresponding dimension:

• If the size of any dimension is 0, then X is an empty array.

• If the size of any dimension is negative, then it is treated as 0.

• Beyond the second dimension, zeros ignores trailing dimensions with a size of 1. For example, zeros([3 1 1 1]) produces a 3-by-1 vector of zeros.

Example: sz = [2 3 4] creates a 2-by-3-by-4 array.

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

typename — Data type (class) to create

'double' (default) | 'single' | 'logical' | 'int8' | 'uint8' | ...

Data type (class) to create, specified as 'double', 'single', 'logical','int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', 'uint64', or the name of another class that provides zeros support.

p — Prototype of array to create

array

Prototype of array to create, specified as an array.

Data Types: double | single | logical | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Dimensions must be nonnegative real integers. Empty dimensions are not supported.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

Refer to the usage notes and limitations in the C/C++ Code Generation section. The same limitations apply to GPU code generation.

HDL Code Generation

Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

Dimensions must be nonnegative real integers.

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 zeros function supports GPU array input with these usage notes and limitations:

• You can specify typename as 'gpuArray'. If you specify typename as 'gpuArray', the default underlying type of the array is double.

  To create a GPU array with underlying type datatype, specify the underlying type as an additional argument before typename. For example, X = zeros(3,datatype,'gpuArray') creates a 3-by-3 GPU array of zeros with underlying type datatype.

  You can specify the underlying type datatype as one of these options:

  • 'double'

  • 'single'

  • 'logical'

  • 'int8'

  • 'uint8'

  • 'int16'

  • 'uint16'

  • 'int32'

  • 'uint32'

  • 'int64'

  • 'uint64'

• You can also specify the numeric variable p as a gpuArray.

  If you specify p as a gpuArray, the underlying type of the returned array is the same as p.

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:

• You can specify typename as 'codistributed' or 'distributed'. If you specify typename as 'codistributed' or 'distributed', the default underlying type of the returned array is double.

  To create a distributed or codistributed array with underlying type datatype, specify the underlying type as an additional argument before typename. For example, X = zeros(3,datatype,'distributed') creates a 3-by-3 distributed matrix of zeros with underlying type datatype.

  You can specify the underlying type datatype as one of these options:

  • 'double'

  • 'single'

  • 'logical'

  • 'int8'

  • 'uint8'

  • 'int16'

  • 'uint16'

  • 'int32'

  • 'uint32'

  • 'int64'

  • 'uint64'

• You can also specify p as a codistributed or distributed array.

  If you specify p as a codistributed or distributed array, the underlying type of the returned array is the same as p.

• For additional codistributed syntaxes, see zeros (codistributed) (Parallel Computing Toolbox).

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

Version History

Introduced before R2006a