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 103 articles , 1 standard article )

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  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. 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)
  3. Daniilidis, Aris; Drusvyatskiy, Dmitriy: Pathological subgradient dynamics (2020)
  4. Fattahi, Salar; Sojoudi, Somayeh: Exact guarantees on the absence of spurious local minima for non-negative rank-1 robust principal component analysis (2020)
  5. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  6. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  7. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  8. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  9. Norkin, V. I.: Generalized gradients in dynamic optimization, optimal control, and machine learning problems (2020)
  10. Welper, G.: Transformed snapshot interpolation with high resolution transforms (2020)
  11. Zheng, Peng; Aravkin, Aleksandr: Relax-and-split method for nonconvex inverse problems (2020)
  12. Fiege, Sabrina; Walther, Andrea; Griewank, Andreas: An algorithm for nonsmooth optimization by successive piecewise linearization (2019)
  13. 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)
  14. Grasedyck, Lars; Krämer, Sebastian: Stable als approximation in the TT-format for rank-adaptive tensor completion (2019)
  15. Hofmeyr, David P.; Pavlidis, Nicos G.; Eckley, Idris A.: Minimum spectral connectivity projection pursuit. Divisive clustering using optimal projections for spectral clustering (2019)
  16. Hosseini, Seyedehsomayeh; Uschmajew, André: A gradient sampling method on algebraic varieties and application to nonsmooth low-rank optimization (2019)
  17. Karmitsa, N.; Gaudioso, M.; Joki, K.: Diagonal bundle method with convex and concave updates for large-scale nonconvex and nonsmooth optimization (2019)
  18. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  19. Knossalla, Martin: Continuous outer subdifferentials in nonsmooth optimization (2019)
  20. Tang, Chunming; Jian, Jinbao; Li, Guoyin: A proximal-projection partial bundle method for convex constrained minimax problems (2019)

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