Sparse Recovery and Compressed Sensing
L1 minimization, restricted isometry property, and recovery guarantees.
Sparse Recovery and Compressed Sensing. L1 minimization, restricted isometry property, and recovery guarantees.
Foundations and canonical references
The standard treatments of sparse recovery and compressed sensing approach the subject from complementary angles. Foucart, A Mathematical Introduction to Compressive Sensing (2013) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to.
Open methodological questions for sparse recovery and compressed sensing include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2013A Mathematical Introduction to Compressive Sensingfoucart-2013, rauhut-2013
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