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I am encountering a precision error with Matlab2020b, which I did not have in version 2016b.

I have 78-dimension vector x (attached). if I do the following, even though the result should be 0, I get a complex number as a result from acos calculation:

> y = x;

> acos(dot(x,y)/sqrt(sum(x.^2)*sum(y.^2)))

ans = 0.0000e+00 + 2.1073e-08i

In Matlab2016b, I know that using "norm" function caused a precision error and acos(dot(x,y)/(norm(x)*norm(y)) gave a complex number.

Back then, the use of sqrt(sum(x.^2)*sum(y.^2)) was a recommended method to avoid this issue. (as summarized in this page: https://stackoverflow.com/questions/36093673/why-do-i-get-a-complex-number-using-acos)

This method has been working fine in 2016b, but now with exactly the same code I have the complex number issue coming back in 2020b.

Was there a change in the precision of calculation in the newer version of matlab? If so, is there any good work around to avoid this issue?

Thanks,

Hiroyuki

Bruno Luong
on 15 Oct 2020

"In Matlab2016b, I know that using "norm" function caused a precision error and acos(dot(x,y)/(norm(x)*norm(y)) gave a complex number.

Back then, the use of sqrt(sum(x.^2)*sum(y.^2)) was a recommended method to avoid this issue. (as summarized in this page: https://stackoverflow.com/questions/36093673/why-do-i-get-a-complex-number-using-acos)

This method has been working fine in 2016b, but now with exactly the same code I have the complex number issue coming back in 2020b."

Pure luck. None of the observation has rigorous justification.

Bruno Luong
on 15 Oct 2020

Edited: Bruno Luong
on 15 Oct 2020

This is a robust code.

theta = acos(max(min(dot(x,y)/sqrt(sum(x.^2)*sum(y.^2)),1),-1))

Note it returns 0 for x or y is 0. One might prefer NaN because correlation is undefined.

Jan
on 20 Oct 2020

The ACOS function is numerically instable at 0 and pi.

SUM is instable at all. A trivial example: sum([1, 1e17, -1]) .There are different approaches to increase the accuracy of the summation, see https://www.mathworks.com/matlabcentral/fileexchange/26800-xsum

There is a similar approach for a stabilized DOT product, but the problem of ACOS will still exist. To determine the angle between two vectors, use a stable ATAN2 method, see https://www.mathworks.com/matlabcentral/answers/471918-angle-between-2-3d-straight-lines#answer_383392

Uday Pradhan
on 15 Oct 2020

Edited: Uday Pradhan
on 16 Oct 2020

Hi Hiroyuki,

If you check (in R2020b):

>> X = dot(x,y) - sqrt(sum(x.^2)*sum(y.^2))

ans =

1.776356839400250e-15

where as in R2016b, we get:

>> dot(x,y) - sqrt(sum(x.^2)*sum(y.^2))

ans =

0

Hence, in R2020b, we get:

>> acos(X)

ans =

0.000000000000000e+00 + 2.107342425544702e-08i

This is because the numerator dot(x,y) is "greater" than the denominator sqrt(sum(x.^2)*sum(y.^2)) albeit by a very small margin and hence the fraction X becomes greater than 1 and thus acos(X) gives complex value.

To avoid this my suggestion would be to establish a threshold precision to measure equality of two variables, for example you could have a check function so that if abs(x-y) < 1e-12 then x = y

function [a,b] = check(x,y)

if abs(x-y) < 1e-12

a = x;

b = a;

end

end

Now, you can do [a,b] = check(x,y) and then call acos(a/b). This will also help in any other function where numerical precision can cause problems.

Hope this helps!

Paul
on 19 Oct 2020

If you look further down in dot.m (2019a) to the section used when dim is specified, you will see that there is a path to

c = sum(conj(a).*b,dim)

even if a and b are both vectors and are isreal. For example

dot(1:3,1:3,1)

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