TY - GEN
T1 - On continuous partial singular value decomposition algorithms
AU - Hasan, Mohammed A.
PY - 2009
Y1 - 2009
N2 - Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimization techniques of unconstrained merit functions. The proposed techniques led to computing low-rank matrix approximation by solving nonlinear matrix differential equations. Numerical experiments illustrate the theoretical results.
AB - Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimization techniques of unconstrained merit functions. The proposed techniques led to computing low-rank matrix approximation by solving nonlinear matrix differential equations. Numerical experiments illustrate the theoretical results.
KW - Matrix approximation
KW - Principal singular subspace
KW - Singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=70350138682&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350138682&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2009.5117887
DO - 10.1109/ISCAS.2009.5117887
M3 - Conference contribution
AN - SCOPUS:70350138682
SN - 9781424438280
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 840
EP - 843
BT - 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
T2 - 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Y2 - 24 May 2009 through 27 May 2009
ER -