The thing is that the only parameters that get updated are those passed to the optimizer. The usual method of passing parameters to the optimizer is via mymodel.parameters(), like this, for example…
optimizer = optim.SGD(mymodel.parameters(), lr=0.01)
You can also add parameters manually using add_param_group like this…
optimizer.add_param_group({'params': A}) # taking A from your example
By wrapping the LinearCombo in a class, I have ensured that if I use it in my model in the usual way its parameters will be included in mymodel.parameters().
That said, if you prefer to use nn.torch.functional to build your model, then you will have to organise the code a little differently, for an example, see here Are torch.nn.Functional layers learnable?