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\({\mathcal V}{\mathcal U}\)-decomposition derivatives for convex max-functions. (English) Zbl 0944.65069
Théra, Michel (ed.) et al., Ill-posed variational problems and regularization techniques. Proceedings of a workshop, Univ. of Trier, Germany, September 3-5, 1998. Berlin: Springer. Lect. Notes Econ. Math. Syst. 477, 167-186 (1999).
Summary: For minimizing a convex max-function \(f\) we consider, at a minimizer, a space decomposition. That is, we distinguish a subspace \({\mathcal V}\), where \(f'\)s nonsmoothness is concentrated, from its orthogonal complement, \({\mathcal U}\). We characterize smooth trajectories, tangent to \({\mathcal U}\), along which \(f\) has a second-order expansion. We give conditions (weaker than typical strong second-order sufficient conditions for optimality) guaranteeing the existence of a Hessian of a related \({\mathcal U}\)-Lagrangian. We also prove, under weak assumptions and for a general convex function, superlinear convergence of a conceptual algorithm for minimizing \(f\) using \({\mathcal V}{\mathcal U}\)-decomposition derivatives.
For the entire collection see [Zbl 0930.00059].

MSC:
65K05 Numerical mathematical programming methods
90C25 Convex programming
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