GRANSO: GRadient-based Algorithm for Non-Smooth Optimization. GRANSO is an optimization package implemented in MATLAB, intended to be efficient for constrained nonsmooth optimization problems, without any special structure or assumptions imposed on the objective or constraint functions. It can handle problems involving functions that are any or all of the following: smooth or nonsmooth, convex or nonconvex, and locally Lipschitz or non-locally Lipschitz.

References in zbMATH (referenced in 23 articles )

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  1. Hund, Manuela; Mitchell, Tim; Mlinarić, Petar; Saak, Jens: Optimization-based parametric model order reduction via (\mathcalH_2\otimes\mathcalL_2) first-order necessary conditions (2022)
  2. Chen, Jingmin; Yu, Thomas; Brogan, Patrick; Kusner, Robert; Yang, Yilin; Zigerelli, Andrew: Numerical methods for biomembranes: conforming subdivision methods versus non-conforming PL methods (2021)
  3. Larson, Jeffrey; Menickelly, Matt; Zhou, Baoyu: Manifold sampling for optimizing nonsmooth nonconvex compositions (2021)
  4. Marrinan, Tim; Absil, P.-A.; Gillis, Nicolas: On a minimum enclosing ball of a collection of linear subspaces (2021)
  5. Noferini, Vanni; Poloni, Federico: Nearest (\Omega)-stable matrix via Riemannian optimization (2021)
  6. Pascal, Barbara; Vaiter, Samuel; Pustelnik, Nelly; Abry, Patrice: Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation (2021)
  7. Aliyev, Nicat; Benner, Peter; Mengi, Emre; Voigt, Matthias: A subspace framework for (\mathcalH_\infty)-norm minimization (2020)
  8. 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)
  9. Birgin, E. G.; Gardenghi, J. L.; Martínez, J. M.; Santos, S. A.: On the use of third-order models with fourth-order regularization for unconstrained optimization (2020)
  10. 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)
  11. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  12. Katewa, Vaibhav; Pasqualetti, Fabio: On the real stability radius of sparse systems (2020)
  13. Tomljanović, Zoran; Voigt, Matthias: Semi-active (\mathcalH_\infty) damping optimization by adaptive interpolation. (2020)
  14. Kürschner, Patrick: Approximate residual-minimizing shift parameters for the low-rank ADI iteration (2019)
  15. Smirnova, Alexandra; Sirb, Benjamin; Chowell, Gerardo: On stable parameter estimation and forecasting in epidemiology by the Levenberg-Marquardt algorithm with Broyden’s rank-one updates for the Jacobian operator (2019)
  16. van Ackooij, Wim; de Oliveira, Welington: Nonsmooth and nonconvex optimization via approximate difference-of-convex decompositions (2019)
  17. Yousefpour, Rohollah; Jafari, Elham: An SQP method for minimization of locally Lipschitz functions with nonlinear constraints (2019)
  18. Benner, Peter; Himpe, Christian; Mitchell, Tim: On reduced input-output dynamic mode decomposition (2018)
  19. Khan, Kamil A.; Larson, Jeffrey; Wild, Stefan M.: Manifold sampling for optimization of nonconvex functions that are piecewise linear compositions of smooth components (2018)
  20. Kolvenbach, Philip; Lass, Oliver; Ulbrich, Stefan: An approach for robust PDE-constrained optimization with application to shape optimization of electrical engines and of dynamic elastic structures under uncertainty (2018)

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