Nonsmooth optimization is traditionally based on convex analysis and most solution methods rely strongly on the convexity of the problem. In this paper, we propose an efficient diagonal bundle method for nonconvex large-scale nonsmooth optimization. The novelty of the new method is in different usage of metrics depending on the convex or concave behaviour of the objective at the current iteration point. The usage of different metrics gives us a possibility to better deal with the nonconvexity of the problem than the sole—the most commonly used and quite arbitrary—downward shifting of the piecewise linear model does. The convergence of the proposed method is proved for semismooth functions that are not necessarily differentiable nor convex. The numerical experiments have been made using problems with up to one million variables. The results to be presented confirm the usability of the new method.

Diagonal bundle method with convex and concave updates for large-scale nonconvex and nonsmooth optimization

Gaudioso, M.;
2019-01-01

Abstract

Nonsmooth optimization is traditionally based on convex analysis and most solution methods rely strongly on the convexity of the problem. In this paper, we propose an efficient diagonal bundle method for nonconvex large-scale nonsmooth optimization. The novelty of the new method is in different usage of metrics depending on the convex or concave behaviour of the objective at the current iteration point. The usage of different metrics gives us a possibility to better deal with the nonconvexity of the problem than the sole—the most commonly used and quite arbitrary—downward shifting of the piecewise linear model does. The convergence of the proposed method is proved for semismooth functions that are not necessarily differentiable nor convex. The numerical experiments have been made using problems with up to one million variables. The results to be presented confirm the usability of the new method.
2019
65K05; 90C06; 90C26; 90C53; bundle methods, diagonal variable metric updates; nonconvex problems; Nondifferentiable optimization; Software; Control and Optimization; Applied Mathematics
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/290192
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact