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:

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  1. Apkarian, Pierre; Noll, Dominikus: Worst-case stability and performance with mixed parametric and dynamic uncertainties (2017)
  2. Helou, Elias Salomão; Santos, Sandra A.; Simões, Lucas E.A.: On the local convergence analysis of the gradient sampling method for finite max-functions (2017)
  3. Hosseini, Seyedehsomayeh; Uschmajew, André: A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds (2017)
  4. Mahdavi-Amiri, N.; Shaeiri, M.: An adaptive competitive penalty method for nonsmooth constrained optimization (2017)
  5. Poirion, Fabrice; Mercier, Quentin; Désidéri, Jean-Antoine: Descent algorithm for nonsmooth stochastic multiobjective optimization (2017)
  6. Sun, Hailin; Su, Che-Lin; Chen, Xiaojun: SAA-regularized methods for multiproduct price optimization under the pure characteristics demand model (2017)
  7. Welper, G.: Interpolation of functions with parameter dependent jumps by transformed snapshots (2017)
  8. Xu, Yangyang; Yin, Wotao: A globally convergent algorithm for nonconvex optimization based on block coordinate update (2017)
  9. Zeng, Jinshan; Peng, Zhiming; Lin, Shaobo: GAITA: A Gauss-Seidel iterative thresholding algorithm for $\ell_q$ regularized least squares regression (2017)
  10. Bigdeli, K.; Hare, W.; Nutini, J.; Tesfamariam, S.: Optimizing damper connectors for adjacent buildings (2016)
  11. Grohs, P.; Hosseini, S.: $\varepsilon$-subgradient algorithms for locally Lipschitz functions on Riemannian manifolds (2016)
  12. Huang, Yakui; Liu, Hongwei: Smoothing projected Barzilai-Borwein method for constrained non-Lipschitz optimization (2016)
  13. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Manifold sampling for $\ell_1$ nonconvex optimization (2016)
  14. Ogura, Masaki; Preciado, Victor M.; Jungers, Raphaël M.: Efficient method for computing lower bounds on the $p$-radius of switched linear systems (2016)
  15. Pavlidis, Nicos G.; Hofmeyr, David P.; Tasoulis, Sotiris K.: Minimum density hyperplanes (2016)
  16. Yousefpour, Rohollah: Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization (2016)
  17. Akbari, Z.; Yousefpour, R.; Reza Peyghami, M.: A new nonsmooth trust region algorithm for locally Lipschitz unconstrained optimization problems (2015)
  18. Coll, Bartomeu; Duran, Joan; Sbert, Catalina: Half-linear regularization for nonconvex image restoration models (2015)
  19. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  20. Drusvyatskiy, Dmitriy; Ioffe, Alexander D.; Lewis, Adrian S.: Clarke subgradients for directionally Lipschitzian stratifiable functions (2015)

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