Abstract
In this paper, a derivative-free Hestenes–Stielfel type method is proposed to solve large-scale nonlinear equations with convex constraints. The proposed method adopts the line search proposed by Ou and Li [J. Comput. Appl. Math. 56(1-2) (2018), pp. 195–216]. Unlike most existing methods, the global convergence of the proposed method is established under the assumption that the underlying mapping is Lipschitz continuous and satisfies a weaker monotonicity condition. Preliminary numerical experiments indicate that the proposed method is effective and promising. Furthermore, the proposed method is used to solve image restoration problem in compressive sensing.
Original language | English |
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Pages (from-to) | 1041-1065 |
Number of pages | 25 |
Journal | International Journal of Computer Mathematics |
Volume | 99 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Keywords
- 65K05
- 65L09
- 90C30
- Unconstrained optimization
- compressive sensing
- conjugate gradient method
- derivative-free method
- nonlinear equations
- projection method