# ManifestΒΆ

Climin was started in the beginning with the observation that plain stochastic gradient descent was not able to find good solutions for sparse filtering [sparsefiltering]. The original article mentions the use of LBFGS as an optimization method, yet the implementation offered by scipy did not solve the problem for us immediately. Since matlab has a very powerful optimization library, we decided that it was time for Python to catch up in this respect.

We found several requirements for a good optimization library.

• Optimization in machine learning is mostly accompanied by online evaluation code: live plotting of error curves, parameters or sending you an email once your model has beaten the current state of the art. Also, you might have your own stopping criterions. We call this the “side logic” of an optimization. Every user has his own way of dealing with this side logic, and a lot of throw away code is being written for this. We wanted to make this part as easy as possible for the user.
• Do one thing, and do that right: climin is independent of your models and the way you work with optimization. We have a simple protocol: loss functions (and their derivatives) and a parameter array. Also, we do not force any framework on you on and come up with things that try to solve everything.
• Most of the optimizers, i.e. those that do not rely on too much linear algebra such as matrix inversions, should not only work on the CPU via numpy but also on the GPU via gnumpy.
• Optimizers should be easily switchable; if we have a model and a loss we wanted to be able to quickly experiment with different methods.
• Optimizers should be reasonably fast. Most of the computational work in machine learning is done within the models anyway. Yet, we want a clean python code base without C extensions. We also found that speeding up everything with Cython would be a good way to go where necessary. Since numba is around the corner, we wanted to decide this in a later version.
• Make development of new optimizers straight forward. The implementation of every optimizer has very little overhead, the most of it being assigning hyper parameters to class values.

The main idea of climin is to treat optimizers as iterators. This allows to have the logic surrounding it right in the same scope and written code block as the optimization. Also, callbacks are really ugly! Python has better tools for that.

 [sparsefiltering] Ngiam, Jiquan, et al. “Sparse filtering.” Advances in Neural Information Processing Systems. 2011.