CUTEst

CUTEst: a constrained and unconstrained testing environment with safe threads. We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named SIFDecode and CUTEst , these updated versions feature dynamic memory allocation, a modern thread-safe Fortran modular design, a new Matlab interface and a revised installation procedure integrated with GALAHAD.


References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Andrea, Caliciotti; Giovanni, Fasano; Massimo, Roma: Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods (2017)
  2. Armand, Paul; Lankoandé, Isaï: An inexact proximal regularization method for unconstrained optimization (2017)
  3. Birgin, E.G.; Martínez, J.M.: The use of quadratic regularization with a cubic descent condition for unconstrained optimization (2017)
  4. Cristofari, Andrea; De Santis, Marianna; Lucidi, Stefano; Rinaldi, Francesco: A two-stage active-set algorithm for bound-constrained optimization (2017)
  5. Fang, Xiaowei; Ni, Qin: A new derivative-free conjugate gradient method for large-scale nonlinear systems of equations (2017)
  6. Francisco, J.B.; Viloche Bazán, F.S.; Weber Mendonça, M.: Non-monotone algorithm for minimization on arbitrary domains with applications to large-scale orthogonal Procrustes problem (2017)
  7. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A stabilized SQP method: superlinear convergence (2017)
  8. Kamandi, Ahmad; Amini, Keyvan; Ahookhosh, Masoud: An improved adaptive trust-region algorithm (2017)
  9. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  10. Ahookhosh, Masoud; Ghaderi, Susan: Two globally convergent nonmonotone trust-region methods for unconstrained optimization (2016)
  11. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization (2016)
  12. Bellavia, Stefania; De Simone, Valentina; di Serafino, Daniela; Morini, Benedetta: On the update of constraint preconditioners for regularized KKT systems (2016)
  13. Birgin, E.G.; Bueno, L.F.; Martínez, J.M.: Sequential equality-constrained optimization for nonlinear programming (2016)
  14. Fasano, Giovanni; Roma, Massimo: A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization (2016)
  15. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  16. Karas, Elizabeth W.; Santos, Sandra A.; Svaiter, Benar F.: Algebraic rules for computing the regularization parameter of the Levenberg-Marquardt method (2016)
  17. Pestana, Jennifer; Rees, Tyrone: Null-space preconditioners for saddle point systems (2016)
  18. Potschka, Andreas: Backward step control for global Newton-type methods (2016)
  19. Rahpeymaii, Farzad; Kimiaei, Morteza; Bagheri, Alireza: A limited memory quasi-Newton trust-region method for box constrained optimization (2016)
  20. Scott, Jennifer; Tuma, Miroslav: Preconditioning of linear least squares by robust incomplete factorization for implicitly held normal equations (2016)

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