Compressive sensing solutions through minimax optimization

Liyi Dai

doi:10.1117/12.2183914

20 May 2015 Compressive sensing solutions through minimax optimization

Liyi Dai

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 94960E (2015) https://doi.org/10.1117/12.2183914
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

This paper is concerned with the basic issue of the robustness of compressive sensing solutions in the presence of uncertainties. In particular, we are interested in robust compressive sensing solutions under unknown modeling and measurement inaccuracies. The problems are formulated as minimax optimization. Exact solutions are derived through the approach of Alternating Direction Method of Multipliers. Numerical examples show the minimax problem formulations indeed improve the robustness of compressive sensing solutions in the presence of model and measurement uncertainties.

Citation Download Citation

Liyi Dai "Compressive sensing solutions through minimax optimization", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960E (20 May 2015); https://doi.org/10.1117/12.2183914

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