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A survey on compressive sensing: classical results and recent advancements | ||
| Journal of Mathematical Modeling | ||
| دوره 8، شماره 3، شهریور 2020، صفحه 309-344 اصل مقاله (760.38 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2020.16701.1450 | ||
| نویسندگان | ||
| Ahmad Mousavi* 1؛ Mehdi Rezaee2؛ Ramin Ayanzadeh2 | ||
| 1Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, MN, USA | ||
| 2Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA | ||
| چکیده | ||
| Recovering sparse signals from linear measurements has demonstrated outstanding utility in a vast variety of real-world applications. Compressive sensing is the topic that studies the associated raised questions for the possibility of a successful recovery. This topic is well-nourished and numerous results are available in the literature. However, their dispersity makes it time-consuming for practitioners to quickly grasp its main ideas and classical algorithms, and further touch upon the recent advancements. In this survey, we overview vital classical tools and algorithms in compressive sensing and describe its significant recent advancements. We conclude by a numerical comparison of the performance of described approaches. | ||
| کلیدواژه‌ها | ||
| Compressive sensing؛ $\ell_p$ recovery؛ greedy algorithms | ||
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