Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm

06/11/2013
by   Myung Cho, et al.
0

In this paper, we propose new efficient algorithms to verify the null space condition in compressed sensing (CS). Given an (n-m) × n (m>0) CS matrix A and a positive k, we are interested in computing α_k = _{z: Az=0,z≠ 0}_{K: |K|≤ k} z_K _1z_1, where K represents subsets of {1,2,...,n}, and |K| is the cardinality of K. In particular, we are interested in finding the maximum k such that α_k < 12. However, computing α_k is known to be extremely challenging. In this paper, we first propose a series of new polynomial-time algorithms to compute upper bounds on α_k. Based on these new polynomial-time algorithms, we further design a new sandwiching algorithm, to compute the exact α_k with greatly reduced complexity. When needed, this new sandwiching algorithm also achieves a smooth tradeoff between computational complexity and result accuracy. Empirical results show the performance improvements of our algorithm over existing known methods; and our algorithm outputs precise values of α_k, with much lower complexity than exhaustive search.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/31/2021

A Hierarchical Stitching Algorithm for Coded Compressed Sensing

Recently, a novel coded compressed sensing (CCS) approach was proposed i...
research
10/27/2019

Compressed Sensing with Probability-based Prior Information

This paper deals with the design of a sensing matrix along with a sparse...
research
06/29/2016

Small coherence implies the weak Null Space Property

In the Compressed Sensing community, it is well known that given a matri...
research
09/10/2018

Optimal variable selection and adaptive noisy Compressed Sensing

For high-dimensional linear regression model, we propose an algorithm of...
research
11/26/2012

Efficient algorithms for robust recovery of images from compressed data

Compressed sensing (CS) is an important theory for sub-Nyquist sampling ...
research
07/17/2013

Universally Elevating the Phase Transition Performance of Compressed Sensing: Non-Isometric Matrices are Not Necessarily Bad Matrices

In compressed sensing problems, ℓ_1 minimization or Basis Pursuit was kn...
research
12/27/2021

Implicit regularity and linear convergence rates for the generalized trust-region subproblem

In this paper we develop efficient first-order algorithms for the genera...

Please sign up or login with your details

Forgot password? Click here to reset