Thanks for the responses. This inaccuracy only occurs every few iterations of my program, and as everything depends on previous iterations, the results after about 1000 iterations are very different from what they used to be when the vector was an Array{Float64, 2}

Maybe if I explain what I’m doing, there might be a simple fix. I have a matrix of asset returns with each row holding one days returns, so I use `mu = transpose(mapslices(mean, exp_tup, [1]))`

to get the column means. This returns an Array{Float64,2}. I only got the floating point inaccuracy when converting the array to a vector using the code `mu = transpose(mapslices(mean, exp_tup, [1]))[1:end]`

which I needed when using the `mu`

in the quadprog function. However, I’ve changed how I go about this conversion and now only convert when calling the function like so: `OptimSemiLog(mu[:,1], sigma, risk_aver_vec[L])`

. I haven’t checked if this results in the floating point inaccuracy but I noticed before it was only occuring when the number of rows = 4

OptimSemiLog is my function which just calls the QuadProg function

```
function OptimSemiLog(mu, Sigma, risk_aver)
A = transpose(ones(size(mu)))
sol = quadprog(mu, Sigma*risk_aver, A, '=', 1, 0, 1, IpoptSolver(print_level=0))
return sol.sol
end
```

If I just left mu, I got a type error.

A further question, how would I go about detecting where these floating point inaccuracies occur? I only picked up on this happening by chance