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Cite Details

Brendt Wohlberg, "SPORCO: A Python package for standard and convolutional sparse representations", in Proceedings of the 15th Python in Science Conference, Katy Huff and David Lippa and Dillon Niederhut and M. Pacer (Eds), (Austin, TX, USA), pp. 1--8, Jul 2017

Abstract

SParse Optimization Research COde (SPORCO) is an open-source Python package for solving optimization problems with sparsity-inducing regularization, consisting primarily of sparse coding and dictionary learning, for both standard and convolutional forms of sparse representation. In the current version, all optimization problems are solved within the Alternating Direction Method of Multipliers (ADMM) framework. SPORCO was developed for applications in signal and image processing, but is also expected to be useful for problems in computer vision, statistics, and machine learning.

BibTeX Entry

@inproceedings{wohlberg-2017-sporco,
author = {Brendt Wohlberg},
title = {{SPORCO}: {A} {P}ython package for standard and convolutional sparse representations},
year = {2017},
month = Jul,
urlhtml = {http://conference.scipy.org/proceedings/scipy2017/brendt_wohlberg.html},
urlpdf = {http://conference.scipy.org/proceedings/scipy2017/pdfs/brendt_wohlberg.pdf},
booktitle = {Proceedings of the 15th Python in Science Conference},
editors = {Katy Huff and David Lippa and Dillon Niederhut and M. Pacer},
address = {Austin, TX, USA},
pages = {1--8}
}