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 96 articles , 1 standard article )

Showing results 1 to 20 of 96.
Sorted by year (citations)

1 2 3 4 5 next

  1. 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)
  2. Daniilidis, Aris; Drusvyatskiy, Dmitriy: Pathological subgradient dynamics (2020)
  3. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  4. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  5. Fiege, Sabrina; Walther, Andrea; Griewank, Andreas: An algorithm for nonsmooth optimization by successive piecewise linearization (2019)
  6. 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)
  7. Grasedyck, Lars; Krämer, Sebastian: Stable als approximation in the TT-format for rank-adaptive tensor completion (2019)
  8. Hofmeyr, David P.; Pavlidis, Nicos G.; Eckley, Idris A.: Minimum spectral connectivity projection pursuit. Divisive clustering using optimal projections for spectral clustering (2019)
  9. Hosseini, Seyedehsomayeh; Uschmajew, André: A gradient sampling method on algebraic varieties and application to nonsmooth low-rank optimization (2019)
  10. Karmitsa, N.; Gaudioso, M.; Joki, K.: Diagonal bundle method with convex and concave updates for large-scale nonconvex and nonsmooth optimization (2019)
  11. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  12. Knossalla, Martin: Continuous outer subdifferentials in nonsmooth optimization (2019)
  13. Tang, Chunming; Jian, Jinbao; Li, Guoyin: A proximal-projection partial bundle method for convex constrained minimax problems (2019)
  14. Yousefpour, Rohollah; Jafari, Elham: An SQP method for minimization of locally Lipschitz functions with nonlinear constraints (2019)
  15. Bruna, Joan; Mallat, Stéphane: Multiscale sparse microcanonical models (2018)
  16. Dolgopolik, M. V.: A convergence analysis of the method of codifferential descent (2018)
  17. Fiege, Sabrina; Walther, Andrea; Kulshreshtha, Kshitij; Griewank, Andreas: Algorithmic differentiation for piecewise smooth functions: a case study for robust optimization (2018)
  18. Greenbaum, Anne; Overton, Michael L.: Numerical investigation of Crouzeix’s conjecture (2018)
  19. Hejazi, M. Alavi; Movahedian, N.; Nobakhtian, S.: On constraint qualifications and sensitivity analysis for general optimization problems via pseudo-Jacobians (2018)
  20. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A fast gradient and function sampling method for finite-max functions (2018)

1 2 3 4 5 next