Lowest Magnitude Eigenvalues of Large Sparse Matrices

Question

I am trying to find the first three lowest eigenvalues of large sparse matrices of size range $10^3 - 10^5$. The matrices depend on some parameter $x$, so I first construct the matrices and then use Eigenvalues[Mat(x), 3]. Mathematica however orders the eigenvalues by absolute value so there is no guarantee in finding the lowest eigenvalue by the method above. See figure below

I can find all the eigenvalues, order them, and then find the minimum using the instructions found here but that defeats the point of using sparse matrices or Mathematica altogether. Moreover, the code slows down even more when cycling through all values of $x$. So, can this problem be done in Mathematica?

Answer 1

To calculate the lowest eigenvalues using Mathematica, I always introduce a "shift" in the following way:

mat1 = mat - IdentityMatrix[Length[mat]]*large

and then add large to the result of Eigenvalues[mat1]. This operation leaves the eigenvectors unchanged and I do not need to use Arnoldi specifically. Of course, Arnoldi does a similar shift inside, however I am not sure if that is used correctly in the Mathematica implementation.

Example:

mat={{1.,2.},{3.,4.}};

The eigenvalues are in the order given by Eigenvalues

{5.37228, -0.372281}.

The procedure above produces for any large enough large:

{-0.372281, 5.37228}

with the smallest first. Or Eigenvalues[mat1,1]+large just the lowest eigenvalue.

Answer 2

Use the Arnoldi method with shift-inversion:

Eigenvalues[A, 3, Method -> {"Arnoldi", "Criteria" -> "Magnitude", "Shift" -> 0}]

gives you the three smallest eigenvalues by absolute value (by magnitude). See here: Efficiently find all values of parameter such that any of the eigenvalues of a matrix is equal to 1

After comments by @HenrikSchumacher it appears that the same can be achieved with

Eigenvalues[A, -3, Method -> {"Arnoldi", "Criteria" -> "Magnitude"}]

and there is no need for explicit shift-inversion.

And as @CarlWoll points out, this method is the default method for sparse matrices, so even

Eigenvalues[A, -3]

achieves the same effect. For non-sparse matrices, however, specifying the method can give a large speedup.

update

It now looks like you want the eigenvalues with smallest real part, not those with smallest magnitude. This you can also achieve with the Arnoldi method:

Eigenvalues[M, 3, Method -> {"Arnoldi", "Criteria" -> "RealPart"}]

gives the three eigenvalues with largest real part. To get the smallest ones, do

-Eigenvalues[-M, 3, Method -> {"Arnoldi", "Criteria" -> "RealPart"}]

The trick is that M and -M have the same eigenvalues (up to sign) and eigenvectors.

Of course this trick also works with the other methods presented above.