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 109 articles , 1 standard article )

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  1. Brown, Jed; He, Yunhui; MacLachlan, Scott; Menickelly, Matt; Wild, Stefan M.: Tuning multigrid methods with robust optimization and local Fourier analysis (2021)
  2. Drusvyatskiy, D.; Ioffe, A. D.; Lewis, A. S.: Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria (2021)
  3. 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)
  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. Daniilidis, Aris; Drusvyatskiy, Dmitriy: Pathological subgradient dynamics (2020)
  6. Fattahi, Salar; Sojoudi, Somayeh: Exact guarantees on the absence of spurious local minima for non-negative rank-1 robust principal component analysis (2020)
  7. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  8. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  9. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  10. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  11. Maleknia, Morteza; Shamsi, Mostafa: A gradient sampling method based on ideal direction for solving nonsmooth optimization problems (2020)
  12. Maleknia, M.; Shamsi, M.: A new method based on the proximal bundle idea and gradient sampling technique for minimizing nonsmooth convex functions (2020)
  13. Michiels, Wim; Fenzi, Luca: Spectrum-based stability analysis and stabilization of time-periodic time-delay systems (2020)
  14. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  15. Norkin, V. I.: Generalized gradients in dynamic optimization, optimal control, and machine learning problems (2020)
  16. Welper, G.: Transformed snapshot interpolation with high resolution transforms (2020)
  17. Zheng, Peng; Aravkin, Aleksandr: Relax-and-split method for nonconvex inverse problems (2020)
  18. Fiege, Sabrina; Walther, Andrea; Griewank, Andreas: An algorithm for nonsmooth optimization by successive piecewise linearization (2019)
  19. Gomez, Marco A.; Michiels, Wim; Mondié, Sabine: Design of delay-based output-feedback controllers optimizing a quadratic cost function via the delay Lyapunov matrix (2019)
  20. Grasedyck, Lars; Krämer, Sebastian: Stable als approximation in the TT-format for rank-adaptive tensor completion (2019)

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