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Hi,

given a variable natural number d, I'm trying to generate a sequence of the form:

[1 2 1 3 2 1 4 3 2 1.......d d-1 d-2......3 2 1].

I don't want to use for loop for this process, does anyone know a better (faster) method. I tried the colon operator without any success.

Thank you.

Adi

Azzi Abdelmalek
on 27 Jul 2013

Edited: Azzi Abdelmalek
on 27 Jul 2013

d=4

cell2mat(arrayfun(@(x) x:-1:1,1:d,'un',0))

Roger Stafford
on 27 Jul 2013

Here's another method to try:

N = d*(d+1)/2;

A = zeros(1,N);

n = 1:d;

A((n.^2-n+2)/2) = n;

A = cumsum(A)-(1:N)+1;

Azzi Abdelmalek
on 28 Jul 2013

Edited: Azzi Abdelmalek
on 28 Jul 2013

Edit

This is twice faster then Stafford's answer

A4=zeros(1,d*(d+1)/2); % Pre-allocate

c=0;

for k=1:d

A4(c+1:c+k)=k:-1:1;

c=c+k;

end

Jan
on 28 Jul 2013

Yes, this is exactly the kind of simplicity, which runs fast. While the one-liners with anonymous functions processed by cellfun or arrayfun look sophisticated, such basic loops hit the point. +1

I'd replace sum(1:d) by: d*(d+1)/2 . Anbd you can omit idx.

Richard Brown
on 29 Jul 2013

Even faster:

k = 1;

n = d*(d+1)/2;

out = zeros(n, 1);

for i = 1:d

for j = i:-1:1

out(k) = j;

k = k + 1;

end

end

Richard Brown
on 29 Jul 2013

I checked again, and I agree with Azzi. My method was running faster because of another case I had in between his and mine. The JIT was doing some kind of unanticipated optimisation between cases.

I get similar orders of magnitude results to Azzi for R2012a if I remove that case, and if I run in R2013a (Linux), his method is twice as fast.

Shame, I like it when JIT brings performance of completely naive loops up to vectorised speed :)

Jan
on 29 Jul 2013

An finally the C-Mex:

#include "mex.h"

void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray*prhs[]) {

mwSize d, i, j;

double *r;

d = (mwSize) mxGetScalar(prhs[0]);

plhs[0] = mxCreateDoubleMatrix(1, d * (d + 1) / 2, mxREAL);

r = mxGetPr(plhs[0]);

for (i = 1; i <= d; i++) {

for (j = i; j != 0; *r++ = j--) ;

}

}

And if your number d can be limited to 65535, the times shrink from 1.9 to 0.34 seconds:

#include "mex.h"

void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray*prhs[]) {

uint16_T d, i, j, *r;

d = (uint16_T) mxGetScalar(prhs[0]);

plhs[0] = mxCreateNumericMatrix(1, d * (d + 1) / 2, mxUINT16_CLASS, mxREAL);

r = (uint16_T *) mxGetData(plhs[0]);

for (i = 1; i <= d; i++) {

for (j = i; j != 0; *r++ = j--) ;

}

}

For UINT32 0.89 seconds are required.

Richard Brown
on 29 Jul 2013

Nice. I imagine d would be limited to less than 65535, that's a pretty huge vector otherwise

Richard Brown
on 29 Jul 2013

Edited: Richard Brown
on 29 Jul 2013

Also comparable, but not (quite) faster

n = 1:(d*(d+1)/2);

a = ceil(0.5*(-1 + sqrt(1 + 8*n)));

out = a.*(a + 1)/2 - n + 1;

Richard Brown
on 29 Jul 2013

If you look at the sequence, and add 0, 1, 2, 3, 4 ... you get

n: 1 2 3 4 5 6 7 8 9 10

1 3 3 6 6 6 10 10 10 10

Note that these are the triangular numbers, and that the triangular numbers 1, 3, 6, 10 appear in their corresponding positions, The a-th triangular number is given by

n = a (a + 1) / 2

So if you solve this quadratic for a where n is a triangular number, you get the index of the triangular number. If you do this for a value of n in between two triangular numbers, you can round this up, and invert the formula to get the nearest triangular number above (which is what the sequence is). Finally, you just subtract the sequence 0, 1, 2, ... to recover the original one.

Andrei Bobrov
on 27 Jul 2013

Edited: Andrei Bobrov
on 30 Jul 2013

out = nonzeros(triu(toeplitz(1:d)));

or

out = bsxfun(@minus,1:d,(0:d-1)');

out = out(out>0);

or

z = 1:d;

z2 = cumsum(z);

z1 = z2 - z + 1;

for jj = d:-1:1

out(z1(jj):z2(jj)) = jj:-1:1;

end

or

out = ones(d*(d+1)/2,1);

ii = cumsum(d:-1:1) - (d:-1:1) + 1;

out(ii(2:end)) = 1-d : -1;

out = flipud(cumsum(out));

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