I am currently running numerical experiments which involve computing the SVD of a very large number of random matrices. Occasionally, Julia’s SVD function will return a Vt factor that contains a row of all NaNs. Here is some code which reproduces this behavior in Julia 1.8.5 and 1.8.1, using updated versions of LinearAlgebra and Random.

```
using LinearAlgebra
using Random
# constructing the matrix
rng = MersenneTwister(17, (0, 181935856, 181934854, 579))
D = Diagonal([ones(100); exp10.(range(0, -12, 50)); 1e-12*ones(100)])
Q = Matrix(qr(D*randn(rng, 250, 150)).Q)
A = Q'*D
svd(A).Vt # 139th row is all NaNs
```

This bug can be difficult to detect, since no error messages are thrown by the SVD to indicate an abnormal result. Running the SVD with `alg = LinearAlgebra.QRIteration()`

fixes the issue, but I believe this algorithm is slower.

I hope this is useful information, and I am curious about the source of the error! Thank you for your time.