I am new to convex optimization and I understand that when the function is convex, it is very easy to find the minimum since no matter where we start, we will always go down to the minimum. But what if that minimum is minus infinity, for example the convex function exp(-x) for x in R and the loss function of logistic regresion, how do we handle these situations?
2026-03-26 14:33:21.1774535601
Convex optimization when the min is minus infinity
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