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A spectral conjugate gradient projection algorithm to solve the large-scale system of monotone nonlinear equations with application to compressed sensing.
- Source :
-
International Journal of Computer Mathematics . Nov2022, Vol. 99 Issue 11, p2290-2307. 18p. - Publication Year :
- 2022
-
Abstract
- In this paper, a derivative-free spectral projection technique to solve a system of large-scale nonlinear monotone equations is presented. The primary motivation is to use the appropriate structure of spectral conjugate gradient directions in the projection algorithms. The new direction is derivative-free and requires a little storage and computation. So, it is an appropriate direction to use in large-scale projection algorithms. We prove the global convergence and R-linear convergence rate of the proposed algorithm under some suitable conditions. Numerical experiments show a promising behaviour of the proposed algorithm to deal with large-scale monotone equations. Additionally, as a practical application, we use the new method to solve the l 1 -norm regularization problems to reconstruct a sparse signal in compressed sensing. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NONLINEAR equations
*COMPRESSED sensing
*CONJUGATE gradient methods
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 00207160
- Volume :
- 99
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 159267615
- Full Text :
- https://doi.org/10.1080/00207160.2022.2047180