TY - JOUR
T1 - Analysis of the Self Projected Matching Pursuit Algorithm
AU - Rebollo-Neira, Laura
AU - Rozlovznik, Miroslav
AU - Sasmal, Pradip
N1 - © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2020/9
Y1 - 2020/9
N2 - The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it is appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
AB - The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it is appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
UR - https://www.sciencedirect.com/science/article/pii/S0016003220304257
UR - http://www.scopus.com/inward/record.url?scp=85087963412&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2020.06.006
DO - 10.1016/j.jfranklin.2020.06.006
M3 - Article
SN - 0016-0032
VL - 357
SP - 8980
EP - 8994
JO - Journal of The Franklin Institute
JF - Journal of The Franklin Institute
IS - 13
ER -