TY - GEN
T1 - Time-Data Tradeoffs in Structured Signals Recovery via the Proximal-Gradient Homotopy Method
AU - Lv, Xiao
AU - Cui, Wei
AU - Liu, Yulong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we characterize data-time tradeoffs of the proximal-gradient homotopy method used for solving linear inverse problems under sub-Gaussian measurements. Our results are sharp up to an absolute constant factor. We demonstrate that, in the absence of the strong convexity assumption, the proximal-gradient homotopy update can achieve a linear rate of convergence when the number of measurements is sufficiently large. Numerical simulations are provided to verify our theoretical results.
AB - In this paper, we characterize data-time tradeoffs of the proximal-gradient homotopy method used for solving linear inverse problems under sub-Gaussian measurements. Our results are sharp up to an absolute constant factor. We demonstrate that, in the absence of the strong convexity assumption, the proximal-gradient homotopy update can achieve a linear rate of convergence when the number of measurements is sufficiently large. Numerical simulations are provided to verify our theoretical results.
UR - http://www.scopus.com/inward/record.url?scp=85136246575&partnerID=8YFLogxK
U2 - 10.1109/ISIT50566.2022.9834548
DO - 10.1109/ISIT50566.2022.9834548
M3 - Conference contribution
AN - SCOPUS:85136246575
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1612
EP - 1616
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022
Y2 - 26 June 2022 through 1 July 2022
ER -