Instructor: Rina Foygel Barber (, office: Eckhart 113)


  • Tue/Thu 10:30-11:50am, Eckhart 117

Office hours:
  • Tue 12-2pm or by appointment

Topics covered:

The material covered in this course will depend on student interest, & will include:
  • Sparse signals & applications
  • Conditions on the measurement matrix for compressed sensing; random measurement matrix
  • Greedy selection & orthogonal matching pursuit
  • Combinatorial group testing
  • L1 minimization for sparse signal recovery
  • Algorithms for L1 minimization
  • Other types of sparsity: block-wise sparsity, fused Lasso, etc.
  • Low-rank matrices & matrix completion
  • Demixing structured signals: Robust PCA & other examples
  • Applications for each topic will be presented & included in HW.

Course information:

  • Prerequisites: familar with linear algebra & probability theory
  • Homework will be assigned every 1-2 weeks, including both theory and programming. HW assignments will include small projects using publicly available data sets. There will be no exams.
  • We will use Matlab in this course (contact instructor if you don't currently have access to Matlab).


Links to many theory / algorithm / application papers:
Boyd & Vandenberghe, Convex Optimization. See Appendix A for a reference on norms, gradients, linear algebra, etc.

What does compressive sensing mean for X-ray CT and comparisons with its MRI application (lecture by Emil Sidky).