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• W2105140177 abstract "A general class of variational models with concave priors is considered for obtaining certain sparse solutions, for which nonsmoothness and non-Lipschitz continuity of the objective functions pose significant challenges from an analytical as well as numerical point of view. For computing a stationary point of the underlying variational problem, a Newton-type scheme with provable convergence properties is proposed. The possible non-positive definiteness of the generalized Hessian is handled by a tailored regularization technique, which is motivated by reweighting as well as the classical trust-region method. Our numerical experiments demonstrate selected applications in image processing, support vector machines, and optimal control of partial differential equations." @default.
• W2105140177 created "2016-06-24" @default.
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• W2105140177 creator A5062202097 @default.
• W2105140177 date "2013-07-13" @default.
• W2105140177 modified "2023-09-24" @default.
• W2105140177 title "A superlinearly convergent R-regularized Newton scheme for variational models with concave sparsity-promoting priors" @default.
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• W2105140177 doi "https://doi.org/10.1007/s10589-013-9583-2" @default.
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