Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1163
Title: Compressive Sensing and its Application
Authors: Maitra, Somak
Keywords: Compressive Sensing
Elementary Theory
Gaussian random matrices
Vectors
Issue Date: 21-Nov-2019
Publisher: IISERM
Abstract: The central problem of the theory of compressive sensing is to reconstruct a sparse vector from its lower dimensional linear measurement. In this thesis, we cover some elementary theory of compressive sensing, including necessary and sufficient conditions to guarantee recovery from underdetermined systems by convex optimization methods. Subsequently, we simulate recovery of sparse vectors from gaussian random matrices and study the trends in error of recovery depending on the number of measurements and sparsity of target vectors. We conclude by studying the performance of sparse vectors in real world systems such spin covariance systems and Optimal Markowitz portfolios.
URI: http://hdl.handle.net/123456789/1163
Appears in Collections:MS-14

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