Data Structures and Algorithms


Non-Negative Quadratic Pursuit

Authors: Babak Hosseini, Barbara Hammer

We propose the Non-negative Quadratic Pursuit (NQP) algorithm to approximately minimize a quadratic function in the presence of the $l0$-norm constraint. It is a quadratic generalization of matching pursuit method which is reformulated in a non-negative framework. Although the optimization problem is NP-hard, NQP provides an approximate solution to the problem which is locally optimal, but acceptable in the general literature. In this document, we explain the algorithm's exact steps along with its convergence proof and complexity analysis.

Comments: 4 Pages. Pre-print introduction for a section of recently submitted article, as provided by the authors.

Download: PDF

Submission history

[v1] 2018-05-29 04:16:47

Unique-IP document downloads: 12 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus