Welcome to adadamp’s documentation!

The optimization underlying machine learning has to handle big data. For example, the MNIST dataset has 60,000 images in the training set, and that’s a small dataset. By comparison, the popular ImageNet dataset has over 10 million images.

Machine learning tries to minimize a “loss function” that depends on every example in the dataset (or conversely, maximize a “quality function”). This requires the gradient of the loss function, which depends on every data. If there aren’t many examples, “gradient descent” is used or a modification thereof.

However, most machine learning models require thousands of gradient computations. To avoid this, machine learning uses stochastic gradient descent (SGD) which approximates the loss function’s gradient with a few examples, aka the batch size. This is nearly universal among different optimization methods.

How should the batch size be selected? Small batch sizes will result in highly variable gradient estimates but can compute many model updates for a given computation budget. Large batch sizes will result in more precise gradient estimate, but can’t compute as many model updates for the same computation budget.

This package provides a method founded in math to balance these two extremes. Use of this package will result in two benefits, at least with a particular setup of a distributed system: machine learning models will be trained more quickly than standard SGD (or competing methods). 1

More detail can be found in the Mathematical underpinnings and Experiments sections.

Indices and tables


Don’t worry – if the distributed setup is not available, this package will not require more floating point operations than standard SGD.