Two new Hager–Zhang iterative schemes with improved parameter choices for monotone nonlinear systems and their applications in compressed sensing
出版年份 2021 全文链接
标题
Two new Hager–Zhang iterative schemes with improved parameter choices for monotone nonlinear systems and their applications in compressed sensing
作者
关键词
-
出版物
RAIRO-OPERATIONS RESEARCH
Volume 56, Issue 1, Pages 239-273
出版商
EDP Sciences
发表日期
2021-12-30
DOI
10.1051/ro/2021190
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Signal recovery with convex constrained nonlinear monotone equations through conjugate gradient hybrid approach
- (2021) Abubakar Sani Halilu et al. MATHEMATICS AND COMPUTERS IN SIMULATION
- A note on memory-less SR1 and memory-less BFGS methods for large-scale unconstrained optimization
- (2021) Neculai Andrei NUMERICAL ALGORITHMS
- On solving double direction methods for convex constrained monotone nonlinear equations with image restoration
- (2021) Abubakar Sani Halilu et al. COMPUTATIONAL & APPLIED MATHEMATICS
- Motion control of the two joint planar robotic manipulators through accelerated Dai–Liao method for solving system of nonlinear equations
- (2021) Abubakar Sani Halilu et al. ENGINEERING COMPUTATIONS
- Two Descent Dai-Yuan Conjugate Gradient Methods for Systems of Monotone Nonlinear Equations
- (2021) Mohammed Yusuf Waziri et al. JOURNAL OF SCIENTIFIC COMPUTING
- Modified Dai-Yuan iterative scheme for nonlinear systems and its application
- (2021) Mohammed Yusuf Waziri et al. Numerical Algebra Control and Optimization
- Descent Perry conjugate gradient methods for systems of monotone nonlinear equations
- (2020) Mohammed Yusuf Waziri et al. NUMERICAL ALGORITHMS
- Two optimal Hager-Zhang conjugate gradient methods for solving monotone nonlinear equations
- (2020) Jamilu Sabi'u et al. APPLIED NUMERICAL MATHEMATICS
- Modified Hager-Zhang conjugate gradient methods via singular value analysis for solving monotone nonlinear equations with convex constraint
- (2020) Jamilu Sabi'u et al. International Journal of Computational Methods
- Modified matrix-free methods for solving system of nonlinear equations
- (2020) Mohammed Yusuf Waziri et al. OPTIMIZATION
- A Modified Fletcher–Reeves Conjugate Gradient Method for Monotone Nonlinear Equations with Some Applications
- (2019) Auwal Bala Abubakar et al. Mathematics
- The Hager–Zhang conjugate gradient algorithm for large-scale nonlinear equations
- (2018) Gonglin Yuan et al. INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
- A new modified scaled conjugate gradient method for large-scale unconstrained optimization with non-convex objective function
- (2018) Zahra Khoshgam et al. OPTIMIZATION METHODS & SOFTWARE
- A derivative-free three-term projection algorithm involving spectral quotient for solving nonlinear monotone equations
- (2018) Gao Peiting et al. OPTIMIZATION
- An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions
- (2017) Mohammad Reza Arazm et al. Glasnik Matematicki
- Accelerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update
- (2017) Neculai Andrei JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
- A spectral algorithm for large-scale systems of nonlinear monotone equations
- (2017) William La Cruz NUMERICAL ALGORITHMS
- A class of conjugate gradient methods for convex constrained monotone equations
- (2017) Yanyun Ding et al. OPTIMIZATION
- A derivative-free projection method for solving convex constrained monotone equations
- (2017) Na Yuan SCIENCEASIA
- A projection method for convex constrained monotone nonlinear equations with applications
- (2015) J.K. Liu et al. COMPUTERS & MATHEMATICS WITH APPLICATIONS
- An Optimal Parameter for Dai–Liao Family of Conjugate Gradient Methods
- (2015) M. Fatemi JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
- Two optimal Dai–Liao conjugate gradient methods
- (2014) Saman Babaie-Kafaki et al. OPTIMIZATION
- The Dai–Liao nonlinear conjugate gradient method with optimal parameter choices
- (2013) Saman Babaie-Kafaki et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing
- (2013) Yunhai Xiao et al. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
- A descent family of Dai–Liao conjugate gradient methods
- (2013) Saman Babaie-Kafaki et al. OPTIMIZATION METHODS & SOFTWARE
- A Scaled Conjugate Gradient Method for Solving Monotone Nonlinear Equations with Convex Constraints
- (2013) Sheng Wang et al. Journal of Applied Mathematics
- Non-smooth equations based method for -norm problems with applications to compressed sensing
- (2011) Yunhai Xiao et al. NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
- Modified nonlinear conjugate gradient method with sufficient descent condition for unconstrained optimization
- (2011) Jinkui Liu et al. JOURNAL OF INEQUALITIES AND APPLICATIONS
- Model-Based Image Reconstruction for MRI
- (2010) Jeffrey Fessler IEEE SIGNAL PROCESSING MAGAZINE
- Image Super-Resolution Via Sparse Representation
- (2010) Jianchao Yang et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Two new conjugate gradient methods based on modified secant equations
- (2010) Saman Babaie-Kafaki et al. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
- Spectral gradient projection method for monotone nonlinear equations with convex constraints
- (2009) Zhensheng Yu et al. APPLIED NUMERICAL MATHEMATICS
- A PRP type method for systems of monotone equations
- (2009) Wanyou Cheng MATHEMATICAL AND COMPUTER MODELLING
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- (2009) Amir Beck et al. SIAM Journal on Imaging Sciences
- Imaging via Compressive Sampling
- (2008) J. Romberg IEEE SIGNAL PROCESSING MAGAZINE
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- (2008) Elaine T. Hale et al. SIAM JOURNAL ON OPTIMIZATION
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