Optimizer Contract

The trainer owns the benchmark epoch. An epoch means one full pass over the training dataset, regardless of the optimizer family.

For gradient methods such as CASSM, GradientStrategy.train_epoch iterates the DataLoader once. Each minibatch runs forward, backpropagation, and one optimizer update.

For EM methods, EMStrategy.train_epoch iterates the DataLoader once to assemble the observations for the method, then calls model.fit_em_epoch(x). That method should perform one full E-step/M-step update using that full pass. Inner linear solves or hyperparameter optimizers inside the M-step are counted inside the same epoch if they reuse the sufficient statistics from that pass. A second full E-step over the dataset is a second benchmark epoch.

GPFA defaults to GradientStrategy: each minibatch computes the exact marginal negative log likelihood and runs one standard PyTorch backward/optimizer step. The older full-dataset EM adapter remains available by setting optimization.name: em.

The reporting contract is shared across strategies: every epoch returns a StepResult, and benchmark plots use seconds_per_epoch. The benchmark records optimizer epoch time only; validation and downstream metric computation are not included in seconds_per_epoch.