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What are the main open problems in compressed sensing?

I am interested in theoretical as well as in numerical point of view.

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    $\begingroup$ This is not a sure finalist for the best question ever competition, of course, but why to downvote (especially without any explanation of what exactly you dislike)???? $\endgroup$ – fedja Dec 20 '14 at 23:17
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    $\begingroup$ There used to be a maxim on MO of the form "MathOverflow is not for people to request other people to write encyclopaedia entries for them". I therefore suggest that Felix should try to narrow his question down first, after looking online at research and survey articles in compressed sensing $\endgroup$ – Yemon Choi Dec 21 '14 at 0:42
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    $\begingroup$ @YemonChoi Well, I have an idea of what compressed sensing is - having read some articles and knowing a thing or two about linear algebra and signal processing, as background. But I cannot really tell which are the more fundamental problems, as opposed to sheer technicalities. That's why I asked for knowledgeable people, to give their estimate of what the main problems are - a bit like my old question mathoverflow.net/questions/118545/… which I think worked rather well. I did not ask anyone to write an encyclopaedia entry for me. $\endgroup$ – Felix Goldberg Dec 21 '14 at 0:53
  • $\begingroup$ One thing is that it makes a huge difference if one answers this question as a mathematician or as an engineer. Well, of course this is site for mathematics but the idea of compressed sensing is really a practical one: get information about a high dimensional object that lives in lower dimensional but nonlinear subspace from few measurements. The meaning of all ingredients is debatable here and varies a lot from problem to problem. Hence one open problem is to find the right mathematical formulation of the problem... $\endgroup$ – Dirk Dec 21 '14 at 13:31
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A relatively recent (2012) overview of open problems and challenges in compressive sensing has been written by Thomas Strohmer. A somewhat older (2007) listing by Terence Tao is still timely:

  • A first question is derandomisation; all of the measurement ensembles for which the really strong compressed sensing results are known have to be generated by a random process; no deterministic compressed sensing method which is rigorously backed by theory is known.
  • Another question is to see whether one can improve the two basic algorithms of basis pursuit and matching pursuit and obtain better results (in either accuracy, speed, or robustness); given the lack of lower bounds in this subject it is difficult to tell how close the current algorithms are to being optimal.
  • A third is to relax the hypotheses on the measurement ensemble, in particular to allow for some limited amount of linear dependence (or near-dependence) between columns of the measuremement matrix.
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