NDLI: ADMM-based approach for compressive sensing with negative weights

Content Provider	IET Digital Library
Author	Xiao, Peng Chu, Ping Liao, Bin
Abstract	In general, weighted compressive sensing recovery needs to solve optimisation problems with the objective function being the sum of a weighted ℓ 1 -norm and a regularised differentiable convex function. Note that the weights in the weight vector are assumed to be positive. In fact, it is possible to achieve outstanding signal recovery performance even if some of the weights are appropriately designed to be negative. However, the negative weights lead to the non-convexity of the optimisation problem, which would place a barrier to attain the optimal solution. To deal with this issue, in this study, the authors propose to solve the problem by applying a well-established algorithm, namely, the alternating direction method of multipliers (ADMM) algorithm. It is shown that the optimal solution can be obtained by taking advantage of this optimisation scheme. The performance of the proposed algorithm is demonstrated by numerical results.
Starting Page	854
Ending Page	860
Page Count	7
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 10, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0276
Journal	IET Signal Processing
Publisher Date	2021-01-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ADMM-based Approach Algebra Compressed Sensing Convex Programming General Recovery Negative Weight Nonconvexity Objective Function Optimal Solution Optimisation Problem Optimisation Scheme Optimisation Technique Regularised Differentiable Convex Function Signal Processing And Detection Signal Processing Theory Signal Recovery Performance Vector Weight Vector Weighted Compressive Sensing Recovery
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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