machine learning - Explanation for Coordinate Descent and Subgradient -
how easy explanation of coordinate descent , subgradient solution in context of lasso.
an intuitive explanation followed proof helpful.
suppose have multivariate function f(w)
k
number of variables/parameters w
(w_1, w_2, w_3, ..., w_k
). parameters knobs , goal change these knobs in way f minimized function f
. coordinate descent greedy method sense on each iteration change values of parameters w_i
minimize f
. easy implement , gradient descent guaranteed minimize f
on each iteration , reach local minima.
picture borrowed internet through bing image search
as shown in picture above, function f
has 2 parameters x
, y
. on each iteration either both of parameters changed fixed value c
, value of function evaluated @ new point. if value higher , goal minimize function, change reversed selected parameter. same procedure done second parameter. 1 iteration of algorithm.
an advantage of using coordinate descent in problems computing gradient of function expensive.
sources
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