CUTEr

CUTEr is a versatile testing environment for optimization and linear algebra solvers. The package contains a collection of test problems, along with Fortran 77, Fortran 90/95 and Matlab tools intended to help developers design, compare and improve new and existing solvers. The test problems provided are written in so-called Standard Input Format (SIF). A decoder to convert from this format into well-defined Fortran 77 and data files is available as a separate package. Once translated, these files may be manipulated to provide tools suitable for testing optimization packages. Ready-to-use interfaces to existing packages, such as MINOS, SNOPT, filterSQP, Knitro, and more, are provided. See the interfaces section for a complete list.


References in zbMATH (referenced in 442 articles , 1 standard article )

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  1. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  2. Babaie-Kafaki, Saman; Ghanbari, Reza: A class of descent four-term extension of the Dai-Liao conjugate gradient method based on the scaled memoryless BFGS update (2017)
  3. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  4. Dong, Xiao Liang; Li, Wei Jun; He, Yu Bo: Some modified Yabe-Takano conjugate gradient methods with sufficient descent condition (2017)
  5. Fang, Xiaowei; Ni, Qin: A frame-based conjugate gradients direct search method with radial basis function interpolation model (2017)
  6. Gao, Huan; Zhang, Hai-Bin; Li, Zhi-Bao; Tadjouddine, Emmanuel: A nonmonotone inexact Newton method for unconstrained optimization (2017)
  7. Huang, Yuanyuan; Liu, Changhe: Dai-Kou type conjugate gradient methods with a line search only using gradient (2017)
  8. Nosratipour, Hadi; Fard, Omid Solaymani; Borzabadi, Akbar Hashemi: An adaptive nonmonotone global Barzilai-Borwein gradient method for unconstrained optimization (2017)
  9. Ou, Yigui; Liu, Yuanwen: A memory gradient method based on the nonmonotone technique (2017)
  10. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  11. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  12. Zhou, Guanghui; Ni, Qin; Zeng, Meilan: A scaled conjugate gradient method with moving asymptotes for unconstrained optimization problems (2017)
  13. Arreckx, Sylvain; Lambe, Andrew; Martins, Joaquim R.R.A.; Orban, Dominique: A matrix-free augmented Lagrangian algorithm with application to large-scale structural design optimization (2016)
  14. Arzani, F.; Peyghami, M.Reza: A new nonmonotone filter Barzilai-Borwein method for solving unconstrained optimization problems (2016)
  15. Babaie-Kafaki, Saman: A modified scaling parameter for the memoryless BFGS updating formula (2016)
  16. Babaie-Kafaki, Saman; Ghanbari, Reza: A descent hybrid modification of the Polak-Ribière-Polyak conjugate gradient method (2016)
  17. Babaie-Kafaki, Saman; Ghanbari, Reza: Descent symmetrization of the Dai-Liao conjugate gradient method (2016)
  18. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  19. Dodangeh, M.; Vicente, L.N.: Worst case complexity of direct search under convexity (2016)
  20. Du, Xuewu; Zhang, Peng; Ma, Wenya: Some modified conjugate gradient methods for unconstrained optimization (2016)

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