Pytorch-minimize

Pytorch-minimize is a library for numerical optimization with automatic differentiation and GPU acceleration. It implements a number of canonical techniques for deterministic (or “full-batch”) optimization not offered in the torch.optim module. The library is inspired heavily by SciPy’s optimize module and MATLAB’s Optimization Toolbox. Unlike SciPy and MATLAB, which use numerical approximations of derivatives that are slow and often inaccurate, pytorch-minimize uses real first- and second-order derivatives, computed seamlessly behind the scenes with autograd. Both CPU and CUDA are supported.

Author

Reuben Feinman

Version

0.0.1

Pytorch-minimize is currently in Beta; expect the API to change before a first official release. Some of the source code was taken directly from SciPy and ported to PyTorch. As such, here is their copyright notice:

Copyright (c) 2001-2002 Enthought, Inc. 2003-2019, SciPy Developers. All rights reserved.

Table of Contents

• Install

• User Guide
  • Using the minimize() function

• API Documentation
  • Functional API
  • Optimizer API

• Examples
  • Unconstrained minimization
  • Constrained minimization
  • Nonlinear least-squares
  • Scipy benchmark
  • Minimizer (optimizer API)