Abstract
In this paper, we present a new hybrid spectral-conjugate gradient (SCG) algorithm for finding approximate solutions to nonlinear monotone operator equations. The hybrid conjugate gradient parameter has the Polak–Ribière–Polyak (PRP), Dai–Yuan (DY), Hestenes–Stiefel (HS) and Fletcher–Reeves (FR) as special cases. Moreover, the spectral parameter is selected such that the search direction has the descent property. Also, the search directions are bounded and the sequence of iterates generated by the new hybrid algorithm converge globally. Furthermore, numerical experiments were conducted on some benchmark nonlinear monotone operator equations to assess the efficiency of the proposed algorithm. Finally, the algorithm is shown to have the ability to recover disturbed signals.
| Original language | English |
|---|---|
| Pages (from-to) | 670-683 |
| Number of pages | 14 |
| Journal | Mathematics and Computers in Simulation |
| Volume | 201 |
| DOIs | |
| State | Published - Nov 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 International Association for Mathematics and Computers in Simulation (IMACS)
Keywords
- Conjugate gradient
- Non-linear equations
- Projection map
- Signal recovery
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science
- Numerical Analysis
- Modeling and Simulation
- Applied Mathematics
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