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I was reading through a paper, Learning Fast Approximations of Sparse Coding (LeCunn, ICML 2010) and ran across a phrase I'm not terribly familiar with. Apologetically, I quote with very little context (though I'll link to a copy of the paper):

[a function described in the paper] is a coordinate-wise shrinking function (...)

Postscript version (ps.gz), and a google-created HTML version.

The question is, what does coordinate-wise mean preferably in a general sense, not just in the context of this paper.

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I've removed the previous edit, as it doesn't appear that the second part of my question applies to the original question.

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    Ah. I see your point. So, if for example I have a scalar function $\sigma$ I want to apply to the elements of a vector/matrix (eg, $\mathbf{Y} = \mathbf{\sigma}(\mathbf{X})$ ), I could say $\mathbf{\sigma}$ is evaluated "coordinate-wise" on elements of $\mathbf{x}$. You should write up your response as an answer so I can accept it.2011-03-22

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As far as I remember, in the context of optimization, "coordinate wise" means that you are manipulating each coordinate independently with the only criterion being that it improves your objective function. So you're zigzagging instead of taking a direct path. I can't remember where I read this, but in some cases, you can go through your coordinates in any order you want and still hit the optimum.

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    @Brian Yes. There is always the matter of something like a learning/step parameter to determine (i.e. how much you're going to push the coordinate in a certain direction) and the magnitude of that step could be an outcome of such functions, maybe it's a constant or random step size or something more sophisticated.2011-03-23