CAAM 567 - Signal Recovery: Theory and Simulation (Spring 2017)


Instructor: Paul E. Hand

Office: Duncan 3086

Email: hand [at]

Lectures: TR, 4-5:15pm in Duncan Hall 1075

Office Hours: Mondays 4-5:30pm and by appointment


This course introduces the theory and numerical algorithms for several fundamental signal recovery tasks. Topics include L1 minimization, sparse regression, compressed sensing, orthogonal matching pursuit, proximal operators, ADMM algorithms, Iterative Reweighted Least Squares. Nuclear norm minimization, matrix completion, robust Principal Component Analysis. The objectives of this course are: (1) to provide you with a firm understanding of the basic tools involved in signal recovery, (2) to improve your ability to design signal recovery algorithms; (3) to improve your ability to prove signal recovery guarantees.

Books: This course will cover Chapter 1 and Chapter 5 of Compressed Sensing: Theory and Applications by Eldar and Kutyniok. It will additionally cover Chapter 5 of Convex Optimization by Boyd and Vandenberghe. It will also cover papers that are freely available from the arXiv. I also recommend All of Statistics by Wasserman.

Class structure and grading: You will have biweekly homework assignments that will be due on Tuesdays. One homework assignments will be pledged and will serve as an exam. One homework will be pledged and will serve as an exam. In groups of three, you will read and present two recent research papers near the end of the semester. Your grade will consist of homeworks (30%), the exam (30%), the paper presentations (30%), and class participation (10%). You are expected to attend class (almost) every day. If you miss more than 4 classes, the classroom participation part of your grade will drop to zero.

Disabilities: Any student with a disability needing academic accommodations is requested to speak with me as soon as possible. All discussions will remain confidential. Students should also contact Disability Support Services in the Ley Student center.



Event Date Related Documents
HW 1Jan 31 in classProblems.
HW 2Feb 21 in classProblems.
HW 3Mar 28 in classProblems.
HW 4Apr 20 in classProblems.


Topics and dates are tentative
Day Topics Reading Assignment Class notes
Jan 10Signal recovery problemsNotes
Jan 12Least squaresNotes
Jan 17Concentration EstimatesWasserman's notes on probability inequalities Notes
Jan 19Concentration EstimatesWasserman's notes on probability inequalities Notes
Jan 24Maximum of GaussiansNotes
Jan 26Chi Squared variablesNotes
Jan 31Spectral Norm of Random MatricesVershynin's notes
Feb 1Spectral Norm of Random MatricesVershynin's notes
Feb 7Spectral Norm of Random MatricesVershynin's notesNotes
Feb 14Convex functions and setsNotes
Feb 16Convex programs and Linear ProgramsNotes
Feb 23Convex duality with equality and inequality constraintsNotes
Feb 28Convex duality with inequality constraintsNotes
Mar 2Cones and conic constraintsNotes
Mar 7Subgradients and L1 optimizationNotes
Mar 9Subgradients and L1 optimizationNotes
Compressed SensingNotes
Null Space PropertyNotes
Compressed Sensing and RIPNotes
Forward Backward MethodsNotes
ADMM for L1Notes
Matrix Completion
Phase Retrieval
Phase Retrieval