Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Authors: James Townsend, Niklas Koep, Sebastian Weichwald
JMLR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce Pymanopt (available at pymanopt.github.io), a toolbox for optimization on manifolds, implemented in Python, that similarly to the Manopt1 Matlab toolbox implements several manifold geometries and optimization algorithms. Moreover, we lower the barriers to users further by using automated differentiation2 for calculating derivative information, saving users time and saving them from potential calculation and implementation errors. Pymanopt enables the user to experiment with different state of the art solvers for optimization problems on manifolds, like the Riemannian Trust Regions solver, without any extra effort. |
| Researcher Affiliation | Academia | James Townsend EMAIL University College London, London, UK Niklas Koep EMAIL RWTH Aachen University, Germany Sebastian Weichwald EMAIL Max Planck Institute for Intelligent Systems, T ubingen, Germany |
| Pseudocode | No | The paper provides a Python code example in Section 4, which is a runnable code snippet demonstrating the usage of the library, not a pseudocode or algorithm block. |
| Open Source Code | Yes | We introduce Pymanopt (available at pymanopt.github.io), a toolbox for optimization on manifolds, implemented in Python... Pymanopt itself and all the required software is open source, with no dependence on proprietary software. The Git Hub repository at github.com/pymanopt/pymanopt offers a convenient way to ask for help or request features by raising an issue, and contains guidelines for those wishing to contribute to the project. |
| Open Datasets | No | The paper introduces a software toolbox and provides an illustrative code example using a generic 'n x n matrix A' for demonstration purposes. It does not conduct experiments on a specific publicly available dataset that requires concrete access information. While it mentions other works using datasets, this paper itself does not provide access to a dataset for its own content. |
| Dataset Splits | No | No information about dataset splits is provided as the paper introduces a software toolbox and provides an illustrative code example rather than conducting experiments on specific datasets requiring splitting. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models or processor types) used for running its examples or development. |
| Software Dependencies | No | Our toolbox is written in Python and uses Num Py and Sci Py for computation and linear algebra operations. Currently Pymanopt is compatible with cost functions defined using Autograd (Maclaurin et al., 2015), Theano (Al-Rfou et al., 2016) or Tensor Flow (Abadi et al., 2015). While several software components are mentioned, their specific version numbers are not provided. |
| Experiment Setup | Yes | def cost(Y): S = np.dot(Y, Y.T) delta = .5 return np.sum(np.sqrt((S A) 2 + delta 2) delta) |