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Notes for the seminar: "The inverse problem in Compressed Sensing"
Compressed Sensing (CS) is a well established theory involving many areas of
modern research. It concerns a family of theoretical techniques and numerical algorithms aimed to recover sparse signals by a partial knowledge of their coefficients.
This process is carried out through minimization problems involving the ℓ1-norm,
that has the property to be convex, while enforcing sparsity. This work aims to
provide the fundamental theory of optimization that lies beneath the many CS
applications, with particular regard to convex minimization and Lagrange theory.
The inverse problem in Compressed Sensing (PDF)
