A family of inertial derivative-free projection methods for constrained nonlinear pseudo-monotone equations with applications
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Title
A family of inertial derivative-free projection methods for constrained nonlinear pseudo-monotone equations with applications
Authors
Keywords
-
Journal
COMPUTATIONAL & APPLIED MATHEMATICS
Volume 41, Issue 7, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-09-13
DOI
10.1007/s40314-022-02019-6
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- A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing
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- Non-smooth equations based method for -norm problems with applications to compressed sensing
- (2011) Yunhai Xiao et al. NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
- Spectral gradient projection method for monotone nonlinear equations with convex constraints
- (2009) Zhensheng Yu et al. APPLIED NUMERICAL MATHEMATICS
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