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 77 articles , 1 standard article )

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  1. Huang, Jianguo; Wang, Chunmei; Wang, Haoqin: A deep learning method for elliptic hemivariational inequalities (2022)
  2. Ackley, Matthew; Stechlinski, Peter: Determining key parameters in riots using lexicographic directional differentiation (2021)
  3. Ackley, Matthew; Stechlinski, Peter: Lexicographic derivatives of nonsmooth glucose-insulin kinetics under normal and artificial pancreatic responses (2021)
  4. Bagirov, Adil M.; Taheri, Sona; Joki, Kaisa; Karmitsa, Napsu; Mäkelä, Marko M.: Aggregate subgradient method for nonsmooth DC optimization (2021)
  5. Eisa, Sameh A.; Stechlinski, Peter: Sensitivity analysis of nonsmooth power control systems with an example of wind turbines (2021)
  6. Stechlinski, Peter: Theory of index-one nonlinear complementarity systems (2021)
  7. Stechlinski, Peter; Barton, Paul I.: Nonsmooth Hessenberg differential-algebraic equations (2021)
  8. Christof, Constantin; De los Reyes, Juan Carlos; Meyer, Christian: A nonsmooth trust-region method for locally Lipschitz functions with application to optimization problems constrained by variational inequalities (2020)
  9. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  10. Stechlinski, Peter: Optimization-constrained differential equations with active set changes (2020)
  11. Woldu, Tsegay Giday; Zhang, Haibin; Zhang, Xin; Fissuh, Yemane Hailu: A modified nonlinear conjugate gradient algorithm for large-scale nonsmooth convex optimization (2020)
  12. Akbari, Z.: An extension of the quasi-Newton method for minimizing locally Lipschitz functions (2019)
  13. Hertlein, Lukas; Ulbrich, Michael: An inexact bundle algorithm for nonconvex nonsmooth minimization in Hilbert space (2019)
  14. Liu, Shuai: A simple version of bundle method with linear programming (2019)
  15. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Vicente, Luis Nunes: Trust-region methods for the derivative-free optimization of nonsmooth black-box functions (2019)
  16. Stechlinski, Peter; Jäschke, Johannes; Barton, Paul I.: Generalized sensitivity analysis of nonlinear programs using a sequence of quadratic programs (2019)
  17. Stechlinski, Peter; Patrascu, Michael; Barton, Paul I.: Nonsmooth DAEs with applications in modeling phase changes (2019)
  18. van Ackooij, Wim; de Oliveira, Welington: Nonsmooth and nonconvex optimization via approximate difference-of-convex decompositions (2019)
  19. Barton, Paul I.; Khan, Kamil A.; Stechlinski, Peter; Watson, Harry A. J.: Computationally relevant generalized derivatives: theory, evaluation and applications (2018)
  20. Gaudioso, Manlio; Giallombardo, Giovanni; Mukhametzhanov, Marat: Numerical infinitesimals in a variable metric method for convex nonsmooth optimization (2018)

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