This library is no longer maintained. I recommend using PyTorch instead of this library.

This library provides object oriented optimization. This allows...

1. using theoretic values (such as the strong convexity parameter)
2. object-oriented definitions, both for models and optimization algorithms. This allows...
   • interacting with the optimization as an object. Want to compute some value partway through? Want to change the values as time goes on?
   • getting results intermediately (or in the presence of a keyboard interrept)
   • having callbacks, etc

A typical example:

def get_stats():
    # ...

model = Model()
opt = SGD(model.loss)

data = []
for _ in range(10):
    opt.step(steps=10)
    data += [get_stats(model)]