Publication:
Basis Pursuit and Linear Programming Equivalence: A Performance Comparison in Sparse Signal Recovery

0
0

Issued Date

2022

Resource Type

Article

Language

eng

File Type

application/pdf

DOI

10.23919/SpliTech55088.2022.9854240

Rights Holder(s)

Scopus

Bibliographic Citation

BMC Pediatrics. Vol 22, No.1 (2022)

Suggested Citation

Tausiesakul B. Basis Pursuit and Linear Programming Equivalence: A Performance Comparison in Sparse Signal Recovery. BMC Pediatrics. Vol 22, No.1 (2022). doi:10.23919/SpliTech55088.2022.9854240 Retrieved from: https://hdl.handle.net/20.500.14740/10342

Title

Basis Pursuit and Linear Programming Equivalence: A Performance Comparison in Sparse Signal Recovery

Author(s)

Tausiesakul B.

Abstract

Basis pursuit (BP) with $\ell_{1}$-norm criterion received much attention in the past. One of its obvious applications is the discrete-time sparse signal acquisition. In this work, two alternative forms of the BP optimization are presented. Both are intended to perform the same task as the BP but are expressed as linear programming (LP) frameworks. The performance of the LP expressions, which are equivalent to the BP, is observed and then compared to that given by the typical BP. It is found that the error performance of the equivalent BP methods in terms of LP is the same as that of the BP algorithm. One of the BP-equivalent LP problems takes the same computational time as the BP, while another lasts longer in computation. In the same manner, the first BP-equivalent LP problem consumes nearly the same amount of required memory as the BP, whereas another occupies significantly more memory space during the computation. © 2022 University of Split, FESB.

Subject(s)

Basis pursuit
 Compressive sensing
 Linear programming

View online Resources

Research Projects

Organizational Units

Journal Issue

URI

https://hdl.handle.net/20.500.14740/10342

Collections

Scopus: Year 2022

Endorsement

Review

Supplemented By

Referenced By