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 77 articles , 1 standard article )

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  1. Gould, Nicholas I. M.; Simoncini, Valeria: Error estimates for iterative algorithms for minimizing regularized quadratic subproblems (2020)
  2. Gratton, S.; Royer, C. W.; Vicente, L. N.: A decoupled first/second-order steps technique for nonconvex nonlinear unconstrained optimization with improved complexity bounds (2020)
  3. Ahmadvand, M.; Esmaeilbeigi, M.; Kamandi, A.; Yaghoobi, F. M.: A novel hybrid trust region algorithm based on nonmonotone and LOOCV techniques (2019)
  4. Ahmadvand, Mohammad; Esmaeilbeigi, Mohsen; Kamandi, Ahmad; Yaghoobi, Farajollah Mohammadi: An improved hybrid-ORBIT algorithm based on point sorting and MLE technique (2019)
  5. Birgin, E. G.; Martínez, J. M.: A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization (2019)
  6. Buttari, Alfredo; Orban, Dominique; Ruiz, Daniel; Titley-Peloquin, David: A tridiagonalization method for symmetric saddle-point systems (2019)
  7. Chen, Xiaojun; Toint, Ph. L.; Wang, H.: Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities (2019)
  8. Cristofari, Andrea; Dehghan Niri, Tayebeh; Lucidi, Stefano: On global minimizers of quadratic functions with cubic regularization (2019)
  9. Curtis, Frank E.; Robinson, Daniel P.: Exploiting negative curvature in deterministic and stochastic optimization (2019)
  10. Dahito, Marie-Ange; Orban, Dominique: The conjugate residual method in linesearch and trust-region methods (2019)
  11. Fatemi, Masoud: A new conjugate gradient method with an efficient memory structure (2019)
  12. Gratton, S.; Royer, C. W.; Vicente, L. N.; Zhang, Z.: Direct search based on probabilistic feasible descent for bound and linearly constrained problems (2019)
  13. Lee, Jae Hwa; Jung, Yoon Mo; Yuan, Ya-xiang; Yun, Sangwoon: A subspace SQP method for equality constrained optimization (2019)
  14. Li, Qun; Zheng, Bing; Zheng, Yutao: An efficient nonmonotone adaptive cubic regularization method with line search for unconstrained optimization problem (2019)
  15. Rahpeymaii, Farzad; Amini, Keyvan; Allahviranloo, Tofigh; Malkhalifeh, Mohsen Rostamy: A new class of conjugate gradient methods for unconstrained smooth optimization and absolute value equations (2019)
  16. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  17. Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien; Peyrega, Mathilde: A progressive barrier derivative-free trust-region algorithm for constrained optimization (2018)
  18. Audet, Charles; Ihaddadene, Amina; Le Digabel, Sébastien; Tribes, Christophe: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm (2018)
  19. Bergou, El Houcine; Diouane, Youssef; Gratton, Serge: A line-search algorithm inspired by the adaptive cubic regularization framework and complexity analysis (2018)
  20. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)

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