Content
- Basic theory of compressed including: sparse solutions to underdetermined linear equations, coherence, Welch-bounds, frames and redundancy, union of bases, nullspace property and best k-term approximation, noisy sparse estimation, the restricted isometry property (RIP), random matrices and the RIP property (stable low-dimensional embeddings), l1-minimization and algorithms (BPDN, LASSO)
- Advanced topics in compressed sensing (partially in the form seminar work and paper reading) including: Structured measurements, Graph-based constructions and special recovery/decoding algorithms, (approximate) message passing algorithms, embdedding and recovery low-dimensional/low-rank signal structures