## Main diagonal operations problem

### Giuseppe Pintori (view profile)

on 13 Sep 2019
Latest activity Edited by Bruno Luong

on 14 Sep 2019

### Stephen Cobeldick (view profile)

Hi guys, I need your help.
I want to create a matrix(4,4) in which the main diagonal have values between 0.3 and 1 and the other cells assume values ​​such as to have a horizontal sum equal to 1.
By now I'm using the following code but the only result is to have a main diagonal composed by the same numbers:
x = eye(4)
x(1,1) = 1+(0.3-1)*rand(1,1)
x(2,2) = x(1,1)
x(3,3) = x(1,1)
x(4,4) = x(1,1)
Any suggestion?
PS : I've tried even with diag

Stephen Cobeldick

### Stephen Cobeldick (view profile)

on 13 Sep 2019
"...the other cells assume values ​​such as to have a horizontal sum equal to 1"
Are there any other requirements on the other elements? Positive, negative, fractional values, integer, >1, >100, >1e100 ... what values are allowed?
What is the "horizontal sum": do you mean to sum along the 2nd dimension (i.e. along each row) ?
Giuseppe Pintori

### Giuseppe Pintori (view profile)

on 13 Sep 2019
the values must be positive and by horizontal sum i mean that the sum of each row must be equal to 1

### Products ### Stephen Cobeldick (view profile)

on 13 Sep 2019
Edited by Stephen Cobeldick

### Stephen Cobeldick (view profile)

on 13 Sep 2019

>> N = 4; % matrix size
>> M = nan(N,N); % preallocate
>> V = 0.3+(1-0.3)*rand(1,1) % diagonal value
V =
0.47505
>> M(~eye(N)) = randfixedsum(N-1,N,1-V,0,1); % other values
>> M = M.'; % transpose
>> M(1:N+1:end) = V % assign diagonal value
M =
0.47505 0.40657 0.0087969 0.10958
0.12917 0.47505 0.21287 0.1829
0.35794 0.15519 0.47505 0.011825
0.41335 0.032696 0.078907 0.47505
Checking the sum of each row:
>> sum(M,2)
ans =
1
1
1
1
and diagonal:
>> diag(M)
ans =
0.47505
0.47505
0.47505
0.47505

Bruno Luong

### Bruno Luong (view profile)

on 13 Sep 2019
@John, I have some formula on the distribution for you (you might know it already).
for X, m vectors of R^n as returned by
X = randfixedsum(n,m,1,0,1)
X(:,i) = { x uniform in [0,1]^n such that sum(x) = 1 }, i=1...,m.
the projection of any component #k, x(k,:) is on [0,1] has the pdf that is a polynomial of order (n-2):
P(x) := (n-1)(1-x).^(n-2) = sum_ {k=0,1..., n-2} p_{n,k} x^k;
with p_{n,k} = (n-1)*(-1)^k* nchoosek(n-2,k);
Example:
n=4;
m = 1e6;
X = randfixedsum(n,m,1,0,1);
P=arrayfun(@(k) (n-1)*(-1)^k*nchoosek(n-2,k),n-2:-1:0);
figure()
ax1 = subplot(2,1,1);
ezplot(ax1,@(x) (n-1)*(1-x).^(n-2),[0 1]);
legend(ax1,"PDF n="+n)
ax2 = subplot(2,1,2);
histogram(X(:),100); % by symmetry all components have the same PDF
xlim(ax1,[0 1]);
ylim(ax1,[0 n-1]);
The brutefoce rejection method from Stephen's (before-edited) code implies the diagonal elements follow the PDF of polynomial form of 2nd order 3*(1-x)^2, truncated bellow 0.3.
In your case as you generate the diagonal as uniform, therefore it's certainly different than the distribution from Stephen's method (polynomial).
The PDF formula can be showed recursively on n, without much difficulty technically.
John D'Errico

### John D'Errico (view profile)

on 13 Sep 2019
Yes. I had come to that conclusion after some thought too. But there still seems to be the question of what is the true goal, since only Giuseppe can know that. I think Stephen's solution comes closer than mine.
On the other hand, HAD I generated the diagonal elements using a better distribution than uniform, then my solution would be an avenue to not needing to use a rejection while loop at all.
Bruno Luong

### Bruno Luong (view profile)

on 13 Sep 2019
Oh I see your point. I think you are right by forcing the right PDF to generate D, it's equivalent to rejection method. This I'm pretty sure it's right because of the special property of simplex and barycentric coordinates.
So the right way (I simplify the procedure to a single row without loosing the generality) is
N = 4;
dmin = 0.3;
dmax = 1;
d = dmax-(dmax-dmin)*rand().^(1/(N-1)); % rather than dmin+(dmax-dmin)*rand;
v = (1-d) * randfixedsum(N-1,1,1,0,1);
then insert d to v.... ### John D'Errico (view profile)

on 13 Sep 2019
Edited by John D'Errico

### John D'Errico (view profile)

on 13 Sep 2019

Easy enough, it seems. First, determine the diagonal elements.
x = diag(rand(1,4)*.7 + .3);
Next, you need to choose the other row elements randomly so the sum will be 1. But that sum will now depend on the diagonal element you just chose. Stilll simple, as long as you use randfixedsum, by Roger Stafford, found on the file exchange.
for i = 1:4
x(i,setdiff(1:4,i)) = randfixedsum(3,1,1 - x(i,i),0,1)';
end
Did it work? Of course.
x
x =
0.83586 0.075979 0.057706 0.030454
0.012356 0.85664 0.11425 0.016757
0.13748 0.21163 0.43081 0.22009
0.15838 0.037488 0.16129 0.64284
>> sum(x,2)
ans =
1
1
1
1
Find randfixedsum here:

### Matt J (view profile)

on 13 Sep 2019

x=eye(4);
x(1:5:end)=0.7*rand(4,1)+0.3

Giuseppe Pintori

### Giuseppe Pintori (view profile)

on 13 Sep 2019
sorry, I forgot to write that the diagonal must be composed of identical values

### Bruno Luong (view profile)

on 13 Sep 2019
Edited by Bruno Luong

### Bruno Luong (view profile)

on 13 Sep 2019

Here is a method that has two advantages:
• without the need of Roger's FEX randfixedsum
• Produce matrix with rigourous uniform conditional probability
N = 4; % matrix size
% diagonal lo/up bounds
dmin = 0.3;
dmax = 1;
% random (common) diagonal value
d = dmax-(dmax-dmin)*rand().^(1/(N-1)); % Edit see comment above, equiv to rejection method
% d = dmin+(dmax-dmin)*rand;
% Generate N random vectors of length N-1 required sum == (1-d)
V = -log(rand(N-1,N)); % Marsaglia's  method
V = V .* ((1-d)./sum(V,1));
% Arrange in the final matrix
A = zeros(N);
isdiag = sparse(1:N,1:N,true);
A(isdiag) = d;
A(~isdiag) = V(:);
A = A.';
% Check result
disp(A)
sum(A,2)
EDIT change after this disccusion with John