Year: 2012
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Davenport, Mark A., Arias-Castro, Ery
Institutions: Stanford University, University of California San Diego
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949920080