Forward-backward truncated Newton methods for convex composite optimization
作者： Alberto Bemporad , Lorenzo Stella , Panagiotis Patrinos
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摘要: This paper proposes two proximal Newton-CG methods for convex nonsmooth optimization problems in composite form. The algorithms are based on aa reformulation of the original nonsmooth problem as the unconstrained minimization of a continuously differentiable function, namely the forward-backward envelope (FBE). The first algorithm is based on a standard line search strategy, whereas the second one combines the global efficiency estimates of the corresponding first-order methods, while achieving fast asymptotic …
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