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.(adsbygoogle = window.adsbygoogle || []).push({});

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.

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# Compressed sensing

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