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Optimization.LineSearch.MirrorDescent
- mirrorDescent :: (Num a, Additive f) => LineSearch f a -> (f a -> f a) -> (f a -> f a) -> (f a -> f a) -> f a -> [f a]
Documentation
mirrorDescent :: (Num a, Additive f) => LineSearch f a -> (f a -> f a) -> (f a -> f a) -> (f a -> f a) -> f a -> [f a]Source
Mirror descent method.
Originally described by Nemirovsky and Yudin and later elucidated
by Beck and Teboulle, the mirror descent method is a generalization of
the projected subgradient method for convex optimization.
Mirror descent requires the gradient of a strongly
convex function psi (and its dual) as well as a way to get a
subgradient for each point of the objective function f.