A robust gradient sampling algorithm for nonsmooth, nonconvex optimization The authors describe a practical and robust algorithm for computing the local minima of a continuously differentiable function in n real variables, which is not convex and not even locally Lipschitz. The only request formulated is that the gradient of the function is easily computed where it is defined. (Source:

References in zbMATH (referenced in 118 articles , 1 standard article )

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  1. Overton, Michael L.: Local minimizers of the Crouzeix ratio: a nonsmooth optimization case study (2022)
  2. Bagirov, A. M.; Hoseini Monjezi, N.; Taheri, S.: An augmented subgradient method for minimizing nonsmooth DC functions (2021)
  3. Brown, Jed; He, Yunhui; MacLachlan, Scott; Menickelly, Matt; Wild, Stefan M.: Tuning multigrid methods with robust optimization and local Fourier analysis (2021)
  4. Burke, J. V.; Lin, Q.: Convergence of the gradient sampling algorithm on directionally Lipschitz functions (2021)
  5. Dinc Yalcin, Gulcin; Kasimbeyli, Refail: Weak subgradient method for solving nonsmooth nonconvex optimization problems (2021)
  6. Drusvyatskiy, D.; Ioffe, A. D.; Lewis, A. S.: Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria (2021)
  7. El Ghali, A.; El Moudden, M.: Reduced subgradient bundle method for linearly constrained non-smooth non-convex problems (2021)
  8. Gebken, Bennet; Peitz, Sebastian: An efficient descent method for locally Lipschitz multiobjective optimization problems (2021)
  9. Larson, Jeffrey; Menickelly, Matt; Zhou, Baoyu: Manifold sampling for optimizing nonsmooth nonconvex compositions (2021)
  10. Li, Xiao; Chen, Shixiang; Deng, Zengde; Qu, Qing; Zhu, Zhihui; Man-Cho So, Anthony: Weakly convex optimization over Stiefel manifold using Riemannian subgradient-type methods (2021)
  11. Asl, Azam; Overton, Michael L.: Analysis of the gradient method with an Armijo-Wolfe line search on a class of non-smooth convex functions (2020)
  12. 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)
  13. Daniilidis, Aris; Drusvyatskiy, Dmitriy: Pathological subgradient dynamics (2020)
  14. Fattahi, Salar; Sojoudi, Somayeh: Exact guarantees on the absence of spurious local minima for non-negative rank-1 robust principal component analysis (2020)
  15. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  16. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  17. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  18. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  19. Maleknia, Morteza; Shamsi, Mostafa: A gradient sampling method based on ideal direction for solving nonsmooth optimization problems (2020)
  20. Maleknia, M.; Shamsi, M.: A new method based on the proximal bundle idea and gradient sampling technique for minimizing nonsmooth convex functions (2020)

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