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Erroneous eigenvalue computation for large matrices in R2016a when forcing single threaded execution

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Why does MATLAB R2016a produce wrong eigenvalues for large matrices (N>7799) when forced to run as a single threaded application? 
Example code
%Diagonalization of a tridiagonal matrix.
t=1; %off-diagonal elements
%creates tridiagonal matrix of size NxN
[V,D]=eig(T,'vector');%compute eigenvalues and store in vector
fprintf('Invoking "eig()" yields eigenvalues:\n')
fprintf('max: %2.4f \n',max(D))
fprintf('min: %2.4f \n',min(D))
Execution Command
taskset -c 0 /opt/MATLAB/R2016a/bin/matlab -nodisplay -nodesktop -nosplash -nojvm -r "N=7800;test"
The above command yields wrong eigenvalues. The correct max and min eigenvalues are supposed to be 2 and -2 respectively.
However, the following multithreaded implementation produces accurate results: 
taskset -c 0,1 /opt/MATLAB/R2016a/bin/matlab -nodisplay -nodesktop -nosplash -nojvm -r "N=7800;test"

Accepted Answer

MathWorks Support Team
MathWorks Support Team on 2 Jan 2018
This error is due to a bug in the Intel Math Kernel Library 11.2.3 that was shipped with MATLAB R2016a. This is a bug that is present only in MATLAB R2016a.
Please upgrade to a different release of MATLAB in order to avoid this error. 

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