TY - GEN
T1 - An Adaptive Sieving Strategy for the Complex Lasso Problem
AU - Bai, Xiaoning
AU - Ye, Yuge
AU - Li, Qingna
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The reconstruction of complex sparse signals is widely used in magnetic resonance imaging, radar and other fields. Most of the current algorithms convert problems with complex variables into real variables, which may cause the special structure of complex numbers to be ignored. In this paper, we propose an adaptive sieving (AS) strategy to effectively reduce the size of complex domain problems. The sieving strategy makes full use of the sparsity of the solution, which can effectively and safely select the nonzero features in the problem and reduce the CPU time. We apply the fast iterative shrinkage-thresholding algorithm (FISTA) to solve the small-scale subproblems after sieving. Numerical results show that the adaptive sieving strategy on FISTA (AS-FISTA) can effectively identify the nonzero features with low computational cost and fast speed.
AB - The reconstruction of complex sparse signals is widely used in magnetic resonance imaging, radar and other fields. Most of the current algorithms convert problems with complex variables into real variables, which may cause the special structure of complex numbers to be ignored. In this paper, we propose an adaptive sieving (AS) strategy to effectively reduce the size of complex domain problems. The sieving strategy makes full use of the sparsity of the solution, which can effectively and safely select the nonzero features in the problem and reduce the CPU time. We apply the fast iterative shrinkage-thresholding algorithm (FISTA) to solve the small-scale subproblems after sieving. Numerical results show that the adaptive sieving strategy on FISTA (AS-FISTA) can effectively identify the nonzero features with low computational cost and fast speed.
KW - Adaptive sieving strategy
KW - Complex lasso
KW - Fast iterative shrinkage-thresholding algorithm
UR - https://www.scopus.com/pages/publications/85186747510
U2 - 10.1109/DSIT60026.2023.00019
DO - 10.1109/DSIT60026.2023.00019
M3 - Conference contribution
AN - SCOPUS:85186747510
T3 - Proceedings - 2023 6th International Conference on Data Science and Information Technology, DSIT 2023
SP - 67
EP - 72
BT - Proceedings - 2023 6th International Conference on Data Science and Information Technology, DSIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Data Science and Information Technology, DSIT 2023
Y2 - 28 July 2023 through 30 July 2023
ER -