optim

Designed specifically for the Torch framework, it integrates smoothly with Torch's autograd and tensor operations, as highlighted in the key features for building ML pipelines.

Follows Torch's modular philosophy, allowing easy customization and combination of optimization routines, which supports implementing custom algorithms as noted in the value proposition.

Includes reliable implementations of common gradient-based optimizers like SGD and L-BFGS, essential for training neural networks as described in the overview.

Provides utilities for logging loss and metrics during training, aiding in monitoring and debugging, which is a key feature mentioned in the documentation links.