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Compressed sensing

  1. Apr 16, 2015 #1
    I finally got a problem in my reading, so my problem is I am given a matrix ##A##, column vector ##f##, and a relation ## f = Ag## where ##g## is a column vector. The dimensions of those vectors and matrices are such that the above matrix multiplication makes sense, that is ## A## is not necessarily square. My goal is to compute ##g## but the number of rows of ##A## is less than the number of columns so that the solution is not unique. However the sought solution is required to be minimum in its ##l_1## norm, where ##l_1## norm of ##g## is defined to be ##||g||_1 = \Sigma_i^N |g_i|##, I guess some of you are familiar with this norm type from linear algebra.
    The above problem is clearly an optimization problem, but as I know there are few types of optimization problem, so if you had recongnized such a problem can you tell me what type of optimization my problem belongs to? So that I can navigate directly to the right chapter in my textbooks.
     
  2. jcsd
  3. Apr 21, 2015 #2
    Thanks for the post! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
     
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