Structured Iterative Hard Thresholding with Off-Grid Applications

12/23/2020
by   Joseph S. Donato, et al.
0

We consider linear sparse recovery problems where additional structure of the sparsity in the solution is known. An algorithm based on iterative hard thresholding is proposed to solve this problem. The convergence and error of the method was analyzed with respect to mutual coherence. Numerical simulations are examined in the context of an inverse source problem, including modifications for off-grid recovery.

READ FULL TEXT

page 12

page 14

page 15

page 16

page 18

research
09/23/2019

Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery

Low-rank tensor recovery problems have been widely studied in many appli...
research
09/25/2018

Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms and Applications to Sparse Reconstruction

This paper deals with the problem of sparse recovery often found in comp...
research
10/29/2019

Learning Sparse Distributions using Iterative Hard Thresholding

Iterative hard thresholding (IHT) is a projected gradient descent algori...
research
06/04/2019

A Nonlinear Acceleration Method for Iterative Algorithms

Iterative methods have led to better understanding and solving problems ...
research
11/08/2019

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

Quadratic regression involves modeling the response as a (generalized) l...
research
01/22/2021

Orthogonal subspace based fast iterative thresholding algorithms for joint sparsity recovery

Sparse signal recoveries from multiple measurement vectors (MMV) with jo...
research
04/11/2022

Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime

We propose a simple modification to the iterative hard thresholding (IHT...

Please sign up or login with your details

Forgot password? Click here to reset