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I'm not sure why the algorithm computes $c_k$ with $\sum_{j \neq k} w_j x_{i, j}$. Why does one need to ignore the $k^{th}$ feature here? I'm not sure how this is derived. Is this the result of taking the gradient with respect to the $k^{th}$ feature?

Also what exactly is $a_k$ and $c_k$?

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    $\begingroup$ I suspect you will need to give us some context: e.g., to tell us what the notation represents, and where you're reading this. Please credit the original source of all copied material: cs.stackexchange.com/help/referencing $\endgroup$ – D.W. Mar 29 at 2:26
  • $\begingroup$ You could also formally state the problem you are trying to optimize. I suspect that it's the least squares regression with Lasso regularization, but the update equations are not what I would expect. $\endgroup$ – Dmitry Mar 29 at 3:37
  • $\begingroup$ This question is more appropriate for Cross Validated, where it might have already been answered: stats.stackexchange.com/questions/123672/…. $\endgroup$ – Yuval Filmus Apr 2 at 6:53

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