# Wrong eigenvalues from a sparse matrix

Bug introduced after 5.0, in or before 8.0 and persisting through 12.0.

I notice in the following example that wrong smallest 2 eigenvalues are resulted if calculating from a sparse matrix. But it gives correct result if we

• calculate the smallest 3,4,... eigenvalues from the sparse matrix
• calculate any smallest eigenvalues from the corresponding normal matrix

I found many cases with this behavior. Why and any remedy?

In the code below, the 104×104 matrix in the minimal example I found is imported from Pastebin because it exceeds the length limit of a post.

Import["https://pastebin.com/raw/PpDfY3EQ", "Package"];
mysparsemat = mymat;
mymat = Normal[mysparsemat];
m = 2;
Reverse@First[Eigensystem[mymat, -m]]
Reverse@First[Eigensystem[mysparsemat, -m]]
m = 4;
Reverse@First[Eigensystem[mymat, -m]]
Reverse@First[Eigensystem[mysparsemat, -m]]

The result of the above code is

{-0.712477 + 1.02863*10^-16 I, 0.712477 + 1.46577*10^-16 I}

{0.712477 - 1.44294*10^-11 I, 0.712656 - 2.12258*10^-11 I}

{-0.712477 + 1.02863*10^-16 I, 0.712477 + 1.46577*10^-16 I,
-0.712656 - 1.05578*10^-16 I, 0.712656 + 6.49144*10^-16 I}

{-0.712477 - 5.10777*10^-10 I, 0.712477 - 5.44863*10^-12 I,
0.712656 + 3.10198*10^-11 I, -0.712656 + 3.64677*10^-10 I}

So the resultant wrong 2nd smallest eigenvalue is actually the correct 3rd or 4th smallest eigenvalue. (The eigenvalues should be real and doubly degenerate in absolute value as expected from the original problem's nature).

Update
As you might have noticed in the comments below, a first guess accusing the Arnoldi algorithm is irrelevant. (And Matlab gives the correct result.)

A bug report has been filed for this in the Wolfram community.

• Please do not use the bugs tag until your observations have been confirmed to be a bug. – J. M.'s technical difficulties Mar 30 '18 at 13:43
• @J. M. Nice edit! Thank you so much! – xiaohuamao Mar 30 '18 at 13:55
• I think the degeneracy is trapping the Arnoldi algorithm. Look at what happens for e.g. Eigenvalues[sm, -2, Method -> {"Arnoldi", "Criteria" -> "Magnitude", "StartingVector" -> SparseArray[1 -> 1, 104]}] and Eigenvalues[sm, -2, Method -> {"Arnoldi", "Criteria" -> "Magnitude", "StartingVector" -> SparseArray[52 -> 1, 104]}]. – J. M.'s technical difficulties Mar 30 '18 at 14:06
• As heretical as this will sound: if you have access to MATLAB, try using eigs() on your matrix. At the very least, it's a way to be sure that it's the algorithm itself that is going flat, and not Mathematica's specific implementation (even tho both are using ARPACK under the hood). – J. M.'s technical difficulties Mar 31 '18 at 1:11
• OK, I asked a friend with MATLAB to run this example. eigs() is perfectly capable of returning the two smallest eigenvalues (in magnitude). I don't know what's up with Mathematica now. – J. M.'s technical difficulties Mar 31 '18 at 8:45

It appears that the proximity of the eigenvalues causes Eigensystem with the default parameters to be inaccurate. This can be fixed by increasing the basis size to 30

Import["https://pastebin.com/raw/PpDfY3EQ", "Package"];
mysparsemat = mymat;
mymat = Normal[mysparsemat];
m = 2;
Reverse[First[Eigensystem[mymat, -m]]]
Reverse[First[Eigensystem[mysparsemat, -m, Method -> {"Arnoldi", "BasisSize" -> 30}]]]

which then gives the desired result

{-0.712477 + 1.02679*10^-16 I, 0.712477 + 2.53294*10^-16 I}

{-0.712477 + 3.34996*10^-16 I, 0.712477 + 1.11772*10^-15 I}