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Difference-of-convex (DC) optimization problems are shown to be equivalent to the minimization of a Lipschitz-differentiable “envelope”. A gradient method on this surrogate function yields a novel (su...b)gradient-free proximal algorithm which is inherently parallelizable and can handle fully nonsmooth formulations. Newton-type methods such as L-BFGS are directly applicable with a classical linesearch. Our analysis reveals a deep kinship between the novel DC envelope and the forward-backward envelope, the former being a smooth and convexity-preserving nonlinear reparametrization of the latter.続きを見る
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Mendeley出力
