I am referring to help I received from Andreas Noack 10 months ago.

https://discourse.julialang.org/t/smallest-eigenvalue-of-a-moler-matrix/10510

I wanted to compute the smallest eigenvalue of Moler matrices with high precision. The example was a Moler matrix of dimension 100.

```
using MatrixDepot
A = matrixdepot("moler", 100);
eigmin(A)
## 2.7755575615628914e-16
```

The smallest eigenvalue is by far not correct. The proposed solution was to convert `A`

to a matrix of “big” numbers.

```
using LinearAlgebra
eigvals(Hermitian(big.(A)))
```

All this was running smoothly with Julia version 0.6, using the *LinearAlgebra* package at that time. Now when I repeat the calculations with Julia 1.1 there is an error saying

```
eigvals(Hermitian(big.(A)))
## ERROR: MethodError: no method matching
## eigvals!(::Hermitian{BigFloat,Array{BigFloat,2}})
```

Has this feature of `LinearAlgebra.eigvals()`

handling big numbers been dropped when integrating the package into Base Julia? What else can/should I do?

Thanks for any help.