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Gradient descent has local maxima problems, so it's not always going to produce an "optimal" result.
by dangerlibrary 10y ago
Gradient descent has local maxima problems, so it's not always going to produce an "optimal" result.
- quotemstr 10y agoHumans also have local maxima problems
- jacobush 10y agoJust this one last cigarette - in the pursuit of happinnes
- merraksh 10y agoGradient ascent reaches a local maximum eventually, but gradient descent is guaranteed to find local minima only.
- no_flags 10y agoThey're the same thing, give or take a minus sign... right?
- merraksh 10y agoIndeed max {f(x): x in X} = - min {-f(x): x in X} However, gradient ascent on a convex minimization problem will get stuck in a local maximum (as a convex minimization problem has f(x) convex, hence with local minima = global minima), and viceversa for gradient descent algorithms on concave maximization problems.