DGM is a Fortran implementation of the discrete gradient method for derivative free optimization. To apply DGM, one only needs to compute at every point the value of the objective function. The subgradient will be approximated. The software is free for academic teaching and research purposes but I ask you to refer the reference given below if you use it.

References in zbMATH (referenced in 44 articles , 1 standard article )

Showing results 1 to 20 of 44.
Sorted by year (citations)

1 2 3 next

  1. Bagirov, Adil M.; Taheri, Sona; Joki, Kaisa; Karmitsa, Napsu; Mäkelä, Marko M.: Aggregate subgradient method for nonsmooth DC optimization (2021)
  2. Dinc Yalcin, Gulcin; Kasimbeyli, Refail: Weak subgradient method for solving nonsmooth nonconvex optimization problems (2021)
  3. Larson, Jeffrey; Menickelly, Matt; Zhou, Baoyu: Manifold sampling for optimizing nonsmooth nonconvex compositions (2021)
  4. 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)
  5. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  6. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  7. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  8. Ordin, Burak; Bagirov, Adil; Mohebi, Ehsan: An incremental nonsmooth optimization algorithm for clustering using (L_1) and (L_\infty) norms (2020)
  9. Bagirov, Adil; Taheri, Sona; Asadi, Soodabeh: A difference of convex optimization algorithm for piecewise linear regression (2019)
  10. Jüngel, Ansgar; Stefanelli, Ulisse; Trussardi, Lara: Two structure-preserving time discretizations for gradient flows (2019)
  11. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  12. Liu, Shuai: A simple version of bundle method with linear programming (2019)
  13. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Vicente, Luis Nunes: Trust-region methods for the derivative-free optimization of nonsmooth black-box functions (2019)
  14. Audet, Charles; Hare, Warren: Algorithmic construction of the subdifferential from directional derivatives (2018)
  15. Bagirov, A. M.; Ugon, J.: Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms (2018)
  16. Dolgopolik, M. V.: A convergence analysis of the method of codifferential descent (2018)
  17. Gaudioso, Manlio; Giallombardo, Giovanni; Mukhametzhanov, Marat: Numerical infinitesimals in a variable metric method for convex nonsmooth optimization (2018)
  18. Golestani, M.; Sadeghi, H.; Tavan, Y.: Nonsmooth multiobjective problems and generalized vector variational inequalities using quasi-efficiency (2018)
  19. Hare, W.; Planiden, C.: Computing proximal points of convex functions with inexact subgradients (2018)
  20. Khan, Kamil A.; Larson, Jeffrey; Wild, Stefan M.: Manifold sampling for optimization of nonconvex functions that are piecewise linear compositions of smooth components (2018)

1 2 3 next