Hi Igor,I'm happy to announce that we have an updated version of TFOCS (version 1.1). It has more building-block functions, more examples, and some bug fixes. We have demos for compressed sensing, image denoising, sparse SVM, matrix completion, and robust PCA, as well as examples for solving arbitrary LP, QP and SDP.The speed is usually competitive with other state-of-the-art first-order methods, and always faster than interior-point methods on large-scale problems.We hope the software will be used like a large-scale CVX (cvxr.com) for people who need to prototype new algorithms.The website is http://tfocs.stanford.edu/. Most of the demos are in the code package, but four of them are also in html format at: http://tfocs.stanford.edu/demos/Best,Stephen
From the current demo page:
RPCA demo (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 Machine (SVM) demo. 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 demo. 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.
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