An improved algorithm for basis pursuit problem and its applications

DOI10.1016/j.amc.2019.02.073zbMath1428.94033OpenAlexW2923167415MaRDI QIDQ2009392

Marko D. Petković, Tanay Saha, Predrag S. Stanimirović, Shwetabh Srivastava, Swanand R. Khare

Publication date: 28 November 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2019.02.073

zbMATH Keywords

signal recoverygeneralized inversecompressive sensingimage deblurringlinearized Bregman iterationsparse solution

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Theory of matrix inversion and generalized inverses (15A09) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Information theory (general) (94A15)

Related Items (4)

Modified bas iteration method for absolute value equation ⋮ Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees ⋮ Existence and Representations of Solutions to Some Constrained Systems of Matrix Equations ⋮ A Laplacian approach to \(\ell_1\)-norm minimization

Cites Work

• Unnamed Item
• Unnamed Item
• A family of higher-order convergent iterative methods for computing the Moore-Penrose inverse
• Iterative method for computing the Moore-Penrose inverse based on Penrose equations
• A chaotic iterative algorithm based on linearized Bregman iteration for image deblurring
• Fast linearized Bregman iteration for compressive sensing and sparse denoising
• A new method for computing Moore-Penrose inverse matrices
• Extensions of compressed sensing
• Accelerated linearized Bregman method
• Quantitative robust uncertainty principles and optimally sparse decompositions
• Successive matrix squaring algorithm for computing outer inverses
• Regularization Preconditioners for Frame-Based Image Deblurring with Reduced Boundary Artifacts
• Theory and applications of compressed sensing
• Augmented $\ell_1$ and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm
• Analysis and Generalizations of the Linearized Bregman Method
• Linearized Bregman iterations for compressed sensing
• Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
• Linearized Bregman Iterations for Frame-Based Image Deblurring
• An Improved Newton Iteration for the Generalized Inverse of a Matrix, with Applications
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• On Iterative Computation of Generalized Inverses and Associated Projections
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• Compressed sensing

This page was built for publication: An improved algorithm for basis pursuit problem and its applications