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Logistics

  • Projects must be done individually.
  • Select a pair among the software packages listed below, and send me an email about your selection. Once you have selected the pair, you must champion one, and your name will appear next to the package in [square brackets]. The second package should be a “different” algorithm. For example, if the first package is an l1-based recovery method, the second should be a non-convex or a greedy recovery method. Once a package is taken, other students will have to select a different package for their project.
  • Your grade will depend on:
    1. Your demonstration of your understanding of the package (through the presentation, report, simulations, etc)
    2. The presentation (organization, clarity, content)
    3. The report (details of the package, simulation results, critical comments)
    4. Any new ideas/results you can add, however small.
  • Please use this style file for writing your reports.
  • Initial project presentation (18 mins per group, 3 mins for questions and changing between speakers): Mar. 19th and 21st, 2014. For your presentation:
    1. First slide: Title of your project (paper), along with its complete reference, and your name, department, and email address.
    2. Briefly discuss the package that you are championing
    3. Present the main algorithm that the package uses. In your opinion, what is are the strengths/weaknesses of this approach?
    4. Make a proposal on what you will show in the final presentation and report
    5. Any other items you wish to include – you have 18 mins!
  • Initial project presentation schedule:
    1. Mar. 19th, 2014: TBD.
    2. Mar. 21st, 2014: TBD.
  • Final project presentation and report: you must aim to answer questions like:
    1. Class of problems the package can solve
    2. Numerical stability and robustness
    3. Ability to handle noisy measurements, non-sparse signals
    4. Phase transition diagrams
    5. Recovery guarantees for the algorithm (theoretical guarantees that can be given)
    6. Precise details of the algorithm
    7. Complexity study, and comparison with the other package
    8. Real-time implementability (can it be parallelized?)
    9. How to set the values of run-time parameters of the algorithm, if any?
    10. A detailed comparative report on the two software packages

Packages

  1. CoSAMP, paper Shubhanshu Shekhar – 2
  2. FOCUSS, k-t FOCUSS, tM FOCUSS
  3. GAMP
  4. L1-LS, user's guide
  5. l1 magic Vikas Kumar Dewangan – 2
  6. SpaRSA
  7. Yall1, user's guide
  8. Zhilin_MSBL
  9. Sparse Bayesian Learning, see also David Wipf's code
  10. L1-homotopy algorithms
  11. SPG L1
  12. NESTA Shubhanshu Shekhar – 1
  13. FPC AS
  14. WaveLab Vikas Kumar Dewangan – 1
  15. GPSR

I will provide links for each of these packages later. Please email me if you find any other package that ought to be included.


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