GradSamp

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: http://plato.asu.edu)


References in zbMATH (referenced in 114 articles , 1 standard article )

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  1. Bagirov, A. M.; Hoseini Monjezi, N.; Taheri, S.: An augmented subgradient method for minimizing nonsmooth DC functions (2021)
  2. Brown, Jed; He, Yunhui; MacLachlan, Scott; Menickelly, Matt; Wild, Stefan M.: Tuning multigrid methods with robust optimization and local Fourier analysis (2021)
  3. Dinc Yalcin, Gulcin; Kasimbeyli, Refail: Weak subgradient method for solving nonsmooth nonconvex optimization problems (2021)
  4. Drusvyatskiy, D.; Ioffe, A. D.; Lewis, A. S.: Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria (2021)
  5. Gebken, Bennet; Peitz, Sebastian: An efficient descent method for locally Lipschitz multiobjective optimization problems (2021)
  6. Larson, Jeffrey; Menickelly, Matt; Zhou, Baoyu: Manifold sampling for optimizing nonsmooth nonconvex compositions (2021)
  7. 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)
  8. 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)
  9. 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)
  10. Daniilidis, Aris; Drusvyatskiy, Dmitriy: Pathological subgradient dynamics (2020)
  11. Fattahi, Salar; Sojoudi, Somayeh: Exact guarantees on the absence of spurious local minima for non-negative rank-1 robust principal component analysis (2020)
  12. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  13. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  14. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  15. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  16. Maleknia, Morteza; Shamsi, Mostafa: A gradient sampling method based on ideal direction for solving nonsmooth optimization problems (2020)
  17. Maleknia, M.; Shamsi, M.: A new method based on the proximal bundle idea and gradient sampling technique for minimizing nonsmooth convex functions (2020)
  18. Michiels, Wim; Fenzi, Luca: Spectrum-based stability analysis and stabilization of time-periodic time-delay systems (2020)
  19. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  20. Norkin, V. I.: Generalized gradients in dynamic optimization, optimal control, and machine learning problems (2020)

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