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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 81 articles , 1 standard article )

Showing results 1 to 20 of 81.
Sorted by year (citations)

1 2 3 4 5 next

  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. Lotfi, Mina; Hosseini, S. Mohammad: An efficient Dai-Liao type conjugate gradient method by reformulating the CG parameter in the search direction equation (2020)
  4. Ahmadvand, M.; Esmaeilbeigi, M.; Kamandi, A.; Yaghoobi, F. M.: A novel hybrid trust region algorithm based on nonmonotone and LOOCV techniques (2019)
  5. Ahmadvand, Mohammad; Esmaeilbeigi, Mohsen; Kamandi, Ahmad; Yaghoobi, Farajollah Mohammadi: An improved hybrid-ORBIT algorithm based on point sorting and MLE technique (2019)
  6. Birgin, E. G.; Martínez, J. M.: A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization (2019)
  7. Buttari, Alfredo; Orban, Dominique; Ruiz, Daniel; Titley-Peloquin, David: A tridiagonalization method for symmetric saddle-point systems (2019)
  8. Cartis, Coralia; Fiala, Jan; Marteau, Benjamin; Roberts, Lindon: Improving the flexibility and robustness of model-based derivative-free optimization solvers (2019)
  9. Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)
  10. Chen, Xiaojun; Toint, Ph. L.; Wang, H.: Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities (2019)
  11. Cristofari, Andrea; Dehghan Niri, Tayebeh; Lucidi, Stefano: On global minimizers of quadratic functions with cubic regularization (2019)
  12. Curtis, Frank E.; Robinson, Daniel P.: Exploiting negative curvature in deterministic and stochastic optimization (2019)
  13. Dahito, Marie-Ange; Orban, Dominique: The conjugate residual method in linesearch and trust-region methods (2019)
  14. Fatemi, Masoud: A new conjugate gradient method with an efficient memory structure (2019)
  15. Gratton, S.; Royer, C. W.; Vicente, L. N.; Zhang, Z.: Direct search based on probabilistic feasible descent for bound and linearly constrained problems (2019)
  16. Kuhlmann, Renke: Learning to steer nonlinear interior-point methods (2019)
  17. Lee, Jae Hwa; Jung, Yoon Mo; Yuan, Ya-xiang; Yun, Sangwoon: A subspace SQP method for equality constrained optimization (2019)
  18. Li, Qun; Zheng, Bing; Zheng, Yutao: An efficient nonmonotone adaptive cubic regularization method with line search for unconstrained optimization problem (2019)
  19. 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)
  20. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)

1 2 3 4 5 next