Mathematics Colloquia and Seminars

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The area of signal processing and information theory has recently experienced a paradigmatic shift due to the theory and techniques of Compressed Sensing (CS). In the ideal case, a signal is encoded in the form of $y = Ax$, while in the case of additive noise we have $y = Ax + e$. Special non-linear methods such as L_1 minimization are often used to recover the original data x. In this talk I will review the basics of CS theory, and will present new results of a general perturbed model of the form: $y = (A+E)x + e$. This model accounts for multiplicative noise as well as additive noise. This area of mathematics relies on simple linear algebra, spectral theory, and harmonic analysis.