Skip to main content

Functional factorization for matrices and tensors

Project description

FunFact

CI Coverage PyPI version Documentation Status License

FunFact is a library for computing the functional factorization of algebraic tensors, a.k.a. multidimensional arrays. A functional factorization, in our context, is a generalization of the (linear) factorization of tensors. By generalization, we meant to replace the standard inner/outer product between the factor tensors with nonlinear operations.

For example, a rank-1 matrix can be factored into the outer product between a column vector and a row vector: $$ M \approx \mathbf{u} \mathbf{v}^\mathsf{T}, $$ where $M$ is an $n \times m$ matrix, $\mathbf{u}$ is a $n$-dimensional column vector, and $\mathbf{v}$ is a $m$-dimensional row vector. This can be equivalently represented in indexed notation as $$ M_{ij} \approx \mathbf{u}_i \mathbf{v}_j. $$ Moreover, if we replace the standard multiplication operation between $\mathbf{u}_i$ and $\mathbf{v}j$ by an RBF function $\kappa(x, y) = \exp\left[-(x - y)^2\right]$, we then obtain an RBF approximation of $M$ such that: $$ M{ij} \approx \kappa(\mathbf{u}_i, \mathbf{v}_j). $$

Given the rich expressivity of nonlinear operators and functional forms, we expect that a proper functional factorization of a tensor can yield representations that are more compact than what is possible withtin the existing linear framework. However, there is (obviously) no free lunch. The challenges to obtain the functional factorization of a tensor is twofold and involves

  • Finding the most appropriate functional form given a specific piece of data,
  • Finding the component tensors given the functional form for a specific data.

The two points above are exactly what we aim to facilitate using FunFact.

Copyright

FunFact Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

funfact-0.7.0.tar.gz (43.0 kB view details)

Uploaded Source

File details

Details for the file funfact-0.7.0.tar.gz.

File metadata

  • Download URL: funfact-0.7.0.tar.gz
  • Upload date:
  • Size: 43.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10

File hashes

Hashes for funfact-0.7.0.tar.gz
Algorithm Hash digest
SHA256 efd87ae77d90c9b249205bc0d1a2d0fd7f66ad40add5ccd00323ccb468c1ff67
MD5 9c0dad2e681ece6272778dea7bbaebcf
BLAKE2b-256 9d7ff17581d319bab5f4831540390cdf74301dc2fd6a13c3233951b5b318ae85

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page