A Preconditioner for A Primal-Dual Newton Conjugate Gradient Method for Compressed Sensing Problems | Zendy

Ioannis Dassios | Zendy; Kimon Fountoulakis | Zendy; Jacek Gondzio | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Preconditioner for A Primal-Dual Newton Conjugate Gradient Method for Compressed Sensing Problems

Author(s) -

Ioannis Dassios,

Kimon Fountoulakis,

Jacek Gondzio

Publication year - 2015

Publication title -

siam journal on scientific computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.674

H-Index - 147

eISSN - 1095-7197

pISSN - 1064-8275

DOI - 10.1137/141002062

Subject(s) - preconditioner , conjugate gradient method , mathematics , compressed sensing , newton's method , numerical analysis , nonlinear conjugate gradient method , dual (grammatical number) , conjugate , conjugate residual method , algorithm , linear system , mathematical optimization , computer science , gradient descent , mathematical analysis , nonlinear system , artificial intelligence , art , physics , literature , quantum mechanics , artificial neural network

In this paper we are concerned with the solution of compressed sensing (CS) problems where the signals to be recovered are sparse in coherent and redundant dictionaries. We extend the primal-dual Newton Conjugate Gradient method (pdNCG) in [T. F. Chan, G. H. Golub, and P. Mulet, SIAM J. Sci. Comput., 20 (1999), pp. 1964--1977] to CS problems. We provide an inexpensive and provably effective preconditioning technique for linear systems using pdNCG. Numerical results are presented on CS problems which demonstrate the performance of pdNCG with the proposed preconditioner compared to state-of-the-art existing solvers.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research