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A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large-Scale Monotone Equations and Its Application in Compressed Sensing
by
Wan, Zhong
, Guo, Jie
in
Algorithms
/ Conjugate gradient method
/ Conjugates
/ Mathematical analysis
/ Nonlinear equations
/ Nonlinear systems
/ Searching
2019
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A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large-Scale Monotone Equations and Its Application in Compressed Sensing
by
Wan, Zhong
, Guo, Jie
in
Algorithms
/ Conjugate gradient method
/ Conjugates
/ Mathematical analysis
/ Nonlinear equations
/ Nonlinear systems
/ Searching
2019
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Do you wish to request the book?
A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large-Scale Monotone Equations and Its Application in Compressed Sensing
by
Wan, Zhong
, Guo, Jie
in
Algorithms
/ Conjugate gradient method
/ Conjugates
/ Mathematical analysis
/ Nonlinear equations
/ Nonlinear systems
/ Searching
2019
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A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large-Scale Monotone Equations and Its Application in Compressed Sensing
Journal Article
A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large-Scale Monotone Equations and Its Application in Compressed Sensing
2019
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Overview
In this paper, we develop an algorithm to solve nonlinear system of monotone equations, which is a combination of a modified spectral PRP (Polak-Ribière-Polyak) conjugate gradient method and a projection method. The search direction in this algorithm is proved to be sufficiently descent for any line search rule. A line search strategy in the literature is modified such that a better step length is more easily obtained without the difficulty of choosing an appropriate weight in the original one. Global convergence of the algorithm is proved under mild assumptions. Numerical tests and preliminary application in recovering sparse signals indicate that the developed algorithm outperforms the state-of-the-art similar algorithms available in the literature, especially for solving large-scale problems and singular ones.
Publisher
Hindawi Publishing Corporation,Hindawi,John Wiley & Sons, Inc
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