tucker-riemopt 1.0.0

Tucker toolbox for Riemannian optimization.

Tucker Riemopt

Python implementation of the Tucker toolbox. Package allows users to manipulate tensors in Tucker and SF-Tucker [1] formats. It also provides tools for implementing first-order optimization methods of the Riemannian optimization on the manifolds of tensors of fixed Tucker rank or fixed SF-Tucker rank. For instance, package implements a method for efficiently computing the Riemannian gradient of any smooth function via automatic differentiation.

The library is compatible with several computation frameworks, such as PyTorch and JAX, and can be easily integrated with other frameworks.

Installation

NumPy, SciPy, PyTorch and opt-einsum are required for installation. Additionally, you need to install your special computation framework (e.g. JAX).

Quick start

See examples folder to dive into tucker_riemopt basics.

• backend notebook contains a guide, how to use different computational frameworks for both routine operations and computations requires autodiff;
• eigenvalues notebook contains a basic guide for performing riemannian optimization on manifold of tensors of fixed multilinear rank using this package;

Documentation

Detailed information may be found here.

License

MIT License