In convex optimization 1:15 it is said that function is non convex and have many local minima and maxima
how the function has so many local maxima and minima
can you please tell it mathematically
SVM classification
If we want to show mathematically that a function is convex or not, we need to take its second derivative, which should always be positive. Here we have one function that is to maximize gamma, with the constraint the yi(wT*xi+b)>=gamma. Optimization of function cannot be done using gradient descend when the function is the constraint, so that is the reason it is called that function is non convex and may contain various maxima and minima.
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