python - ConvergenceWarning: lbfgs failed to converge (status=1): STOP . . . lbfgs stand for: "Limited-memory Broyden–Fletcher–Goldfarb–Shanno Algorithm" It is one of the solvers' algorithms provided by Scikit-Learn Library The term limited-memory simply means it stores only a few vectors that represent the gradients approximation implicitly
The reason of superiority of Limited-memory BFGS over ADAM solver I noticed that using the solver lbfgs (I guess it implies Limited-memory BFGS in scikit learn) outperforms ADAM when the dataset is relatively small (less than 100K) Can someone provide a concrete justification for that? In fact, I couldn't find a good resource that explains the reason behind that
Trouble using tfp. optimizer. lbfgs_minimize in a PINN model Other implementations are linked in the header of tfp_optimizer py They define a helper class that will dynamically associate the weights of the neural network to a vector containing the flatten and concatenated parameters and give it to the optimizer After the LBFGS optimization the optimized weights are then applied back to the neural network
MLPRegressor learning_rate_init for lbfgs solver in sklearn LBFGS is an optimization algorithm that simply does not use a learning rate For the purpose of your school project, you should use either sgd or adam Regarding whether it makes more sense or not, I would say that training a neural network on 20 data points doesn't make a lot of sense anyway, except for learning the basics
How can I use the LBFGS optimizer with pytorch ignite? I started using Ignite recently and i found it very interesting I would like to train a model using as an optimizer the LBFGS algorithm from the torch optim module This is my code: from ignite en