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LDGB

swMATH ID: 7134
Software Authors: Haarala, M.; Miettinen, K.; M"akel"a, M.M.
Description: New limited memory bundle method for large-scale nonsmooth optimization Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables. In such problems the direct application of smooth gradient-based methods may lead to a failure due to the nonsmooth nature of the problem. On the other hand, none of the current general nonsmooth optimization methods is efficient in large-scale settings. In this article we describe a new limited memory variable metric based bundle method for nonsmooth large-scale optimization. In addition, we introduce a new set of academic test problems for large-scale nonsmooth minimization. Finally, we give some encouraging results from numerical experiments using both academic and practical test problems.
Homepage: http://napsu.karmitsa.fi/ldgbm/
Keywords: nondifferentiable programming; large-scale optimization; bundle methods; variable metric methods; limited memory methods; test problems
Related Software: GradSamp; PNEW; QSM; DGM; L-BFGS; PBNCGC; UFO; MPBNGC; SQPlab; PLCP; SolvOpt; NSO; LMBM; SCALCG; Matlab; CG_DESCENT; LBFGS-B; UCI-ml; PDCO; NDA
Cited in: 47 Documents
Further Publications: http://napsu.karmitsa.fi/publications/

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