# Can anyone explain me why M' is taken in the following code?

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Aniket Bordekar on 1 Aug 2020
Commented: Stephen23 on 22 Dec 2022
%{
Question : Write a function called minimax that takes M, a matrix input argument and
returns mmr, a row vector containing the absolute values of the difference
between the maximum and minimum valued elements in each row. As a second
output argument called mmm, it provides the difference between the maximum
and minimum element in the entire matrix
%}
%Code :
function [mmr, mmm] = minimax(M)
mmr = max(M') - min(M');
mmm = max(M, [], 'all') - min(M, [], 'all');
Stephen23 on 22 Dec 2022

Walter Roberson on 2 Aug 2020
By default, min() and max() operate along the first non-scalar dimension. If you have a 2D array, m x n, with m and n both not 1, then that means that min() or max() of the array would produce a 1 x n output -- it has operated along columns, producing one result for each column.
Now suppose you transpose the m x n array to become n x m, with the rows becoming columns and the columns becoming rows, and you min() or max that. You will get a 1 x m result -- one result for each of what were originally rows.
Thus, min(M') is one way of producing a minimum for each row (but it has a problem if the data is complex-valued.)
More clear and robust is to use the syntax min(M,[],2) to process dimension #2 specifically, producing one result for each row.

Shubham Shah on 22 Dec 2022
function [mmr,mmm]= minimax(M)
mmt = [max(M,[],2)-min(M,[],2)]
mmr = mmt'
mmm = max(M, [], 'all') - min(M, [], 'all')
end
Shubham Shah on 22 Dec 2022
Question : Write a function called minimax that takes M, a matrix input argument and
returns mmr, a row vector containing the absolute values of the difference
between the maximum and minimum valued elements in each row. As a second
output argument called mmm, it provides the difference between the maximum
and minimum element in the entire matrix