deletions | additions       diff --git a/Theory.tex b/Theory.tex  index a1c1ee9..6ac183c 100644  --- a/Theory.tex  +++ b/Theory.tex 
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\ i,j \in W \ ,  \end{equation}  where the 1-norm is considered as a \emph{vector} norm  (\(||A||_1 = \sum_{i,j} \left|A_{ij}\right|\)), and \(W\) is a set of randomly chosen matrix entries. The size of \(W\) \(W\), that we call \(M\)  is the number of matrix elements of \(B\) that we need to sample, sample  and it will determine determines  the success quality  of the reconstruction. From compressed sensing theory we can find a lower bound to the size  One important requirement for compressed sensing is that the basis  $\{\psi_i\}$ for \(A\) and the basis $\{\phi_i\}$ for \(B\) should be