DocumentCode
3119350
Title
Compressive binary search
Author
Davenport, Mark A. ; Arias-Castro, Ery
Author_Institution
Dept. of Stat., Stanford Univ., Stanford, CA, USA
fYear
2012
fDate
1-6 July 2012
Firstpage
1827
Lastpage
1831
Abstract
In this paper we consider the problem of locating a nonzero entry in a high-dimensional vector from possibly adaptive linear measurements. We consider a recursive bisection method which we dub the compressive binary search and show that it improves on what any nonadaptive method can achieve. We also establish a non-asymptotic lower bound that applies to all methods, regardless of their computational complexity. Combined, these results show that the compressive binary search is within a double logarithmic factor of the optimal performance.
Keywords
computational complexity; recursive functions; vectors; adaptive linear measurement; compressive binary search; computational complexity; double logarithmic factor; high-dimensional vector; nonadaptive method; nonasymptotic lower bound; recursive bisection method; Algorithm design and analysis; Context; Matching pursuit algorithms; Noise measurement; Sensors; Testing; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
Conference_Location
Cambridge, MA
ISSN
2157-8095
Print_ISBN
978-1-4673-2580-6
Electronic_ISBN
2157-8095
Type
conf
DOI
10.1109/ISIT.2012.6283595
Filename
6283595
Link To Document