Bug introduced after 5.0, in or before 8.0 and persisting through 13.3.

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).

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.

  • $\begingroup$ Please do not use the bugs tag until your observations have been confirmed to be a bug. $\endgroup$ Commented Mar 30, 2018 at 13:43
  • $\begingroup$ @J. M. Nice edit! Thank you so much! $\endgroup$
    – xiaohuamao
    Commented Mar 30, 2018 at 13:55
  • 3
    $\begingroup$ 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]}]. $\endgroup$ Commented Mar 30, 2018 at 14:06
  • 2
    $\begingroup$ 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). $\endgroup$ Commented Mar 31, 2018 at 1:11
  • 7
    $\begingroup$ 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. $\endgroup$ Commented Mar 31, 2018 at 8:45

2 Answers 2


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}

I found a similar issue in a non-numeric caluclation:

sol = C[1] Cos[\[Lambda] x] + C[2] Sin[\[Lambda] x] + C[3] Cosh[\[Lambda] x] + C[4] Sinh[\[Lambda] x];
row1 = CoefficientArrays[sol /. {x -> 0}, {C[1], C[2], C[3], C[4]}][[2]];
row2 = CoefficientArrays[D[sol, x] /. {x -> 0}, {C[1], C[2], C[3], C[4]}][[2]];
row3 = CoefficientArrays[D[sol, x, x] /. {x -> l}, {C[1], C[2], C[3], C[4]}][[2]];
row4 = CoefficientArrays[D[sol, x, x, x] /. {x -> l}, {C[1], C[2], C[3], C[4]}][[2]];

sparsematrix = {row1, row2, row3, row4};
normalmatrix = Normal@sparsematrix;
Det[normalmatrix]// FullSimplify

It gives 0 and 2 \[Lambda]^6 (1 + Cos[l \[Lambda]] Cosh[l \[Lambda]]). The first result is unexpected. It took me a while to figure that out that Det[] does different things and fails when sparse vectors are collected as normalform-matrix. It can be fixed by Det[SparseArray[sparsematrix]]. I consider this a bug. Det[] apparently understands that the matrix is 4 by 4 (leaving out one row in the above example causes Det[] to complain about the matrix not being square). So one assumes that it is handled properly, but the result is just wrong.

  • $\begingroup$ I doubt this is related to the original issue in this thread. It should be reported separately. $\endgroup$ Commented Sep 29, 2020 at 4:48
  • $\begingroup$ I see. Where can I file a bug report? $\endgroup$ Commented Sep 30, 2020 at 9:40
  • $\begingroup$ Actually I can just file it. I think for external reports it might be [email protected]. Also poking around the web site I found this: wolfram.com/support/contact/email/?topic=feedback $\endgroup$ Commented Sep 30, 2020 at 17:28
  • 1
    $\begingroup$ Report now filed. $\endgroup$ Commented Sep 30, 2020 at 17:37
  • $\begingroup$ This is fixed now. Det[SparseArray[sparsematrix]] gives the same as in Det[sparsematrix] (not 0, but $\lambda ^6 \sin ^2(\lambda l)+\lambda ^6 \cos ^2(\lambda l)-\lambda ^6 \sinh ^2(\lambda l)+\lambda ^6 \cosh ^2(\lambda l)+2 \lambda ^6 \cos (\lambda l) \cosh (\lambda l)$) $\endgroup$ Commented Dec 15, 2021 at 11:59

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