PNEW

Algorithm 811: NDA: algorithms for nondifferentiable optimization We present four basic Fortran subroutines for nondifferentiable optimization with simple bounds and general linear constraints. Subroutine PMIN, intended for minimax optimization, is based on a sequential quadratic programming variable metric algorithm. Subroutines PBUN and PNEW, intended for general nonsmooth problems, are based on bundle-type methods. Subroutine PVAR is based on special nonsmooth variable metric methods. Besides the description of methods and codes, we propose computational experiments which demonstrate the efficiency of this approach.


References in zbMATH (referenced in 66 articles , 1 standard article )

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  1. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  2. Woldu, Tsegay Giday; Zhang, Haibin; Zhang, Xin; Fissuh, Yemane Hailu: A modified nonlinear conjugate gradient algorithm for large-scale nonsmooth convex optimization (2020)
  3. Hertlein, Lukas; Ulbrich, Michael: An inexact bundle algorithm for nonconvex nonsmooth minimization in Hilbert space (2019)
  4. Liu, Shuai: A simple version of bundle method with linear programming (2019)
  5. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Vicente, Luis Nunes: Trust-region methods for the derivative-free optimization of nonsmooth black-box functions (2019)
  6. Stechlinski, Peter; Jäschke, Johannes; Barton, Paul I.: Generalized sensitivity analysis of nonlinear programs using a sequence of quadratic programs (2019)
  7. van Ackooij, Wim; de Oliveira, Welington: Nonsmooth and nonconvex optimization via approximate difference-of-convex decompositions (2019)
  8. Barton, Paul I.; Khan, Kamil A.; Stechlinski, Peter; Watson, Harry A. J.: Computationally relevant generalized derivatives: theory, evaluation and applications (2018)
  9. Gaudioso, Manlio; Giallombardo, Giovanni; Mukhametzhanov, Marat: Numerical infinitesimals in a variable metric method for convex nonsmooth optimization (2018)
  10. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A fast gradient and function sampling method for finite-max functions (2018)
  11. Lv, Jian; Pang, Li-Ping; Meng, Fan-Yun: A proximal bundle method for constrained nonsmooth nonconvex optimization with inexact information (2018)
  12. Sheng, Zhou; Yuan, Gonglin: An effective adaptive trust region algorithm for nonsmooth minimization (2018)
  13. Astorino, A.; Gaudioso, M.; Gorgone, E.: A method for convex minimization based on translated first-order approximations (2017)
  14. Fendl, Hannes; Neumaier, Arnold; Schichl, Hermann: Certificates of infeasibility via nonsmooth optimization (2017)
  15. Mahdavi-Amiri, N.; Shaeiri, M.: An adaptive competitive penalty method for nonsmooth constrained optimization (2017)
  16. Ovcharova, Nina: On the coupling of regularization techniques and the boundary element method for a hemivariational inequality modelling a delamination problem (2017)
  17. Dao, Minh N.; Gwinner, Joachim; Noll, Dominikus; Ovcharova, Nina: Nonconvex bundle method with application to a delamination problem (2016)
  18. Drori, Yoel; Teboulle, Marc: An optimal variant of Kelley’s cutting-plane method (2016)
  19. Hare, W.; Sagastizábal, C.; Solodov, M.: A proximal bundle method for nonsmooth nonconvex functions with inexact information (2016)
  20. Nagesseur, Ludovic: A bundle method using two polyhedral approximations of the (\epsilon)-enlargement of a maximal monotone operator (2016)

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