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Identifying Active Manifolds in Regularization Problems

  • W. L. Hare
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 49)

Abstract

In 2009, Tseng and Yun [Math. Programming (Ser. B) 117, 387–423 (2009)], showed that the regularization problem of minimizing f(x) +  | ​ | x | ​ | 1, where f is a \({\mathcal{C}}^{2}\) function and | ​ | x | ​ | 1 is the l 1 norm of x, can be approached by minimizing the sum of a quadratic approximation of f and the l 1 norm. We consider a generalization of this problem, in which the l 1 norm is replaced by a more general nonsmooth function that contains an underlying smooth substructure. In particular, we consider the problem
$${ \min }_{x}\{f(x) + P(x)\},$$
(13.1)
where f is \({\mathcal{C}}^{2}\) and P is prox-regular and partly smooth with respect to an active manifold \(\mathcal{M}\) (the l 1 norm satisfies these conditions.) We reexamine Tseng and Yun’s algorithm in terms of active set identification, showing that their method will correctly identify the active manifold in a finite number of iterations. That is, after a finite number of iterations, all future iterates x k will satisfy \({x}^{k} \in \mathcal{M}\). Furthermore, we confirm a conjecture of Tseng that, regardless of what technique is used to solve the original problem, the subproblem \({p}^{k} =\mathrm{{ argmin}}_{p}\{\langle \nabla f({x}^{k}),p\rangle + \frac{r} {2}\vert {x}^{k} - p{\vert }^{2} + P(p)\}\) will correctly identify the active manifold in a finite number of iterations.

Keywords

Nonconvex optimization Active constraint identification Prox-regular Partly smooth 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsKelownaCanada

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