I was wondering whether there is a smart and efficient way in Matlab to compute the kronecker product of several 1D arrays.

What I mean is something like this

A = [a1, a2];
B = [b1, b2];
C = [c1, c2];
K = f(A,B,C) = [a1*b1*c1, a1*b1*c2, a1*b2*c1, ... ]

One possible way it is to use the kron(X,Y) function, but it has to be put inside a loop to obtain the aforementioned result since the kron function accepts only 2 arguments per time.

K = 1;
tot_arrays = [A, B, C];
for i=1:num_arrays
    K = kron(K,tot_arrays(:,i));

Isn't there a smarter and more efficient way to obtain the same result for the kron multiplication of more than 2 arrays per time?


  • $\begingroup$ Have you already seen Fast Kronecker matrix multiplication in the exchange? $\endgroup$ Sep 18, 2017 at 15:59
  • $\begingroup$ Hi @MauroVanzetto, thanks for the reply. No, I never heard about it. However, after a quick check at the link you provided, it does not seem to be what I'm looking for. Thanks $\endgroup$
    – FancyPants
    Sep 20, 2017 at 21:55

1 Answer 1


Faster to write: I don't think there is. Faster to run with larger array lengths: the first thing I would try is the following.

>> a = [1 2];
>> b = [3 4];
>> c = [5 6];
>> a = reshape(a, [length(a) 1 1]);
>> b = reshape(b, [1 length(b) 1]);
>> c = reshape(c, [1 1 length(c)]);
>> P = a.*b.*c;
>> P = reshape(P, [1 length(P)])
P =
    15    30    20    40    18    36    24    48
  • $\begingroup$ Thanks for the reply. Which version of Matlab did you use to test this script? I tried it, but it does not seem to work. $\endgroup$
    – FancyPants
    Sep 20, 2017 at 21:54
  • $\begingroup$ @FancyPants It should work starting from R2016b. There have been some changes to how operators expand singleton dimensions there, which is necessary for a.*b.*c to work. If you have an older version, you have to use bsxfun instead to get the same behavior, but that works only with two arrays at the time. $\endgroup$ Sep 20, 2017 at 22:00
  • $\begingroup$ @FancyPants If you type edit kron.m you'll see the definition similar to this. You can generalize it from there by removing the sparse stuff. $\endgroup$
    – percusse
    Sep 21, 2017 at 9:28

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