Hello,

I was wondering if it is possible to solve minimax optimization problems using `Optimization.jl`

for neural networks? I am currently performing the optimization using `Optimization.solve(...)`

. I am specifically interested in multiplying the gradients of the loss function with respect to *some* parameters, by -1 so that the loss function is maximized with respect to those specific parameters and minimized with respect to the rest of the parameters. I would assume that this would have to be done in the callback function since I am using `solve(...)`

? Just for clarification, my code is very long and I would prefer to keep it in the current format with `Optimization.solve(...)`

.