cummin

Cumulative minimum

Syntax

M = cummin(A)

M = cummin(A,dim)

M = cummin(___,direction)

M = cummin(___,nanflag)

Description

M = cummin(A) returns the cumulative minimum elements of A. By default, cummin(A) operates along the first array dimension whose size does not equal 1.

• If A is a vector, then cummin(A) returns a vector of the same size containing the cumulative minima of A.

• If A is a matrix, then cummin(A) returns a matrix of the same size containing the cumulative minima in each column of A.

• If A is a multidimensional array, then cummin(A) returns an array of the same size containing the cumulative minima along the first array dimension of A whose size does not equal 1.

M = cummin(A,dim) returns the cumulative minima along the dimension dim. For example, if A is a matrix, then cummin(A,2) returns the cumulative minima along the rows of A.

M = cummin(___,direction) optionally specifies the direction using any of the previous syntaxes. You must specify A and, optionally, can specify dim. For instance, cummin(A,2,'reverse') returns the cumulative minima of A by working from end to beginning of the second dimension of A.

M = cummin(___,nanflag) specifies whether to include or omit NaN values from the calculation for any of the previous syntaxes. cummin(A,'includenan') includes all NaN values in the calculation while cummin(A,'omitnan') ignores them.

Examples

Cumulative Minimum Values in Vector

Find the cumulative minima of a 1-by-10 vector of random integers.

v = randi([0,10],1,10)

v = 1×10

     8     9     1    10     6     1     3     6    10    10

M = cummin(v)

M = 1×10

     8     8     1     1     1     1     1     1     1     1

Cumulative Minimum Values in Matrix Columns

Find the cumulative minima of the columns of a 3-by-3 matrix.

A = [3 5 2; 1 6 3; 7 8 1]

A = 3×3

     3     5     2
     1     6     3
     7     8     1

M = cummin(A)

M = 3×3

     3     5     2
     1     5     2
     1     5     1

Cumulative Minimum Values in Matrix Rows

Find the cumulative minima of the rows of a 3-by-3 matrix.

A = [3 5 2; 1 6 3; 7 8 1]

A = 3×3

     3     5     2
     1     6     3
     7     8     1

M = cummin(A,2)

M = 3×3

     3     3     2
     1     1     1
     7     7     1

Cumulative Minimum Array Values in Reverse Direction

Calculate the cumulative minima in the third dimension of a 2-by-2-by-3 array. Specify direction as 'reverse' to work from the end of the third dimension to the beginning.

A = cat(3,[1 2; 3 4],[9 10; 11 12],[5 6; 7 8])

A = 
A(:,:,1) =

     1     2
     3     4


A(:,:,2) =

     9    10
    11    12


A(:,:,3) =

     5     6
     7     8

M = cummin(A,3,'reverse')

M = 
M(:,:,1) =

     1     2
     3     4


M(:,:,2) =

     5     6
     7     8


M(:,:,3) =

     5     6
     7     8

Vector with NaN Values

Create a vector containing NaN values and compute the cumulative minima. By default, cummin ignores NaN values.

A = [3 5 NaN 9 0 NaN];
M = cummin(A)

M = 1×6

     3     3     3     3     0     0

If you include NaN values in the calculation, then the cumulative minimum becomes NaN as soon as the first NaN value in A is encountered.

M = cummin(A,'includenan')

M = 1×6

     3     3   NaN   NaN   NaN   NaN

Input Arguments

A — Input array

vector | matrix | multidimensional array

Input array, specified as a vector, matrix, or multidimensional array. For complex elements, cummin compares the magnitude of the elements. If magnitudes are equal, cummin also compares the phase angles.

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

dim — Dimension to operate along

positive integer scalar

Dimension to operate along, specified as a positive integer scalar. If no value is specified, then the default is the first array dimension whose size does not equal 1.

Consider a two-dimensional input array, A:

• cummin(A,1) works on successive elements in the columns of A and returns an array of the same size as A with the cumulative minima in each column.

  

• cummin(A,2) works on successive elements in the rows of A and returns an array of the same size as A with the cumulative minima in each row.

  

cummin returns A if dim is greater than ndims(A).

direction — Direction of cumulation

'forward' (default) | 'reverse'

Direction of cumulation, specified as 'forward' (default) or 'reverse'.

• 'forward' works from 1 to end of the active dimension.

• 'reverse' works from end to 1 of the active dimension.

Data Types: char

nanflag — NaN condition

'omitnan' (default) | 'includenan'

NaN condition, specified as one of the following values:

• 'omitnan' — Ignore all NaN values in the input. If the input has consecutive leading NaN values, then cummin returns NaN in the corresponding elements of the output. For example, cummin([NaN 7 13 6],'omitnan') returns the row vector [NaN 7 7 6].

• 'includenan' — Include NaN values from the input when computing the cumulative minima, resulting in NaN values in the output.

Data Types: char

Output Arguments

M — Cumulative minima

vector | matrix | multidimensional array

Cumulative minima, returned as a vector, matrix, or multidimensional array. The size and data type of M are the same as those of A.

Tips

• The 'reverse' option in many cumulative functions allows quick directional calculations without requiring a flip or reflection of the input array.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function supports tall arrays with the limitations:

The 'reverse' direction is not supported.

For more information, see Tall Arrays.

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

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

GPU Arrays

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

Usage notes and limitations:

• The nanflag argument is not supported.

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).