Matrix sparsification and non-negative factorization for task partitioning in computational sensing and imaging

David G. Stork*, Neda Rohani, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We address the mathematical foundations of a special case of the general problem of partitioning an end-to-end sensing algorithm for implementation by optics and by a digital processor for minimal electrical power dissipation. Specifically, we present a non-iterative algorithm for factoring a general k × k real matrix A (describing the end-to-end linear pre-processing) into the product BC, where C has no negative entries (for implementation in linear optics) and B is maximally sparse, i.e., has the fewest possible non-zero entries (for minimal dissipation of electrical power). Our algorithm achieves a sparsification of B: i.e., the number s of non-zero entries in B: of s ≤ 2k, which we prove is optimal for our class of problems.

Original languageEnglish (US)
Title of host publicationComputational Imaging II
EditorsAmit Ashok, Lei Tian, Abhijit Mahalanobis, Jonathan C. Petruccelli, Kenneth S. Kubala
PublisherSPIE
ISBN (Electronic)9781510609457
DOIs
StatePublished - 2017
EventComputational Imaging II 2017 - Anaheim, United States
Duration: Apr 9 2017Apr 10 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10222
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherComputational Imaging II 2017
CountryUnited States
CityAnaheim
Period4/9/174/10/17

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Matrix sparsification and non-negative factorization for task partitioning in computational sensing and imaging'. Together they form a unique fingerprint.

Cite this