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 54 articles , 1 standard article )

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  1. Hosseini, Seyedehsomayeh; Uschmajew, André: A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds (2017)
  2. Grohs, P.; Hosseini, S.: $\varepsilon$-subgradient algorithms for locally Lipschitz functions on Riemannian manifolds (2016)
  3. Huang, Yakui; Liu, Hongwei: Smoothing projected Barzilai-Borwein method for constrained non-Lipschitz optimization (2016)
  4. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Manifold sampling for $\ell_1$ nonconvex optimization (2016)
  5. Ogura, Masaki; Preciado, Victor M.; Jungers, Raphaël M.: Efficient method for computing lower bounds on the $p$-radius of switched linear systems (2016)
  6. Pavlidis, Nicos G.; Hofmeyr, David P.; Tasoulis, Sotiris K.: Minimum density hyperplanes (2016)
  7. Yousefpour, Rohollah: Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization (2016)
  8. Akbari, Z.; Yousefpour, R.; Reza Peyghami, M.: A new nonsmooth trust region algorithm for locally Lipschitz unconstrained optimization problems (2015)
  9. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  10. Huang, Yakui; Liu, Hongwei; Cong, Weijie: A note on the smoothing quadratic regularization method for non-Lipschitz optimization (2015)
  11. Karmitsa, Napsu: Diagonal bundle method for nonsmooth sparse optimization (2015)
  12. Borckmans, Pierre B.; Easter Selvan, S.; Boumal, Nicolas; Absil, P.-A.: A Riemannian subgradient algorithm for economic dispatch with valve-point effect (2014)
  13. Hinow, Peter: A nonsmooth program for jamming hard spheres (2014)
  14. Hintermüller, Michael; Wu, Tao: A superlinearly convergent $R$-regularized Newton scheme for variational models with concave sparsity-promoting priors (2014)
  15. Lin, Gui-Hua; Xu, Mengwei; Ye, Jane J.: On solving simple bilevel programs with a nonconvex lower level program (2014)
  16. Tang, Chun-Ming; Liu, Shuai; Jian, Jin-Bao; Li, Jian-Ling: A feasible SQP-GS algorithm for nonconvex, nonsmooth constrained optimization (2014)
  17. Xu, Mengwei; Ye, Jane J.: A smoothing augmented Lagrangian method for solving simple bilevel programs (2014)
  18. Garmanjani, R.; Vicente, L.N.: Smoothing and worst-case complexity for direct-search methods in nonsmooth optimization (2013)
  19. Hare, Warren; Macklem, Mason: Derivative-free optimization methods for finite minimax problems (2013)
  20. Hare, W.; Nutini, J.: A derivative-free approximate gradient sampling algorithm for finite minimax problems (2013)

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