- RPCA (from SIAM OPT 11 conference). This shows how to use TFOCS to perform Robust Principal Component Analysis. For a background on RPCA, see Robust Principal Component Analysis? by J. Candès, X. Li, Y. Ma, and J. Wright, in Journal of ACM 58(1), 1-37.
- Support Vector Machines (SVM). This covers basic SVM as well as a type of sparse-SVM. For a background in SVM, there are many online resources; a good introduction is chapter 8.6 of the free online textbook Convex Optimization by Stephen Boyd and Lieven Vandenberghe (2004).
- Matrix completion. This demonstrates recovering a low-rank matrix from partially observed entries via nuclear norm minimization. For a background on nuclear norm minimization, see Exact matrix completion via convex optimization by E. J. Candès and B. Recht, in Found. of Comput. Math., 9 717-772.
- Alternating projections. Covers three methods (alternating directions, Dykstra’s projection algorithm, and TFOCS) for projecting a point onto the intersection of two convex sets. For some problems, TFOCS is extremely efficient compared to the alternative methods. Demo added July 17 2012.
