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 469 articles , 1 standard article )

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  1. Amaioua, Nadir; Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien: Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm (2018)
  2. Andrei, Neculai: A Dai-Liao conjugate gradient algorithm with clustering of eigenvalues (2018)
  3. Andrei, Neculai: A double parameter scaled BFGS method for unconstrained optimization (2018)
  4. Huyer, Waltraud; Neumaier, Arnold: MINQ8: general definite and bound constrained indefinite quadratic programming (2018)
  5. Li, Dan; Zhu, Detong: An affine scaling interior trust-region method combining with line search filter technique for optimization subject to bounds on variables (2018)
  6. Li, Min: A modified Hestense-Stiefel conjugate gradient method close to the memoryless BFGS quasi-Newton method (2018)
  7. Öztoprak, Figen; Birbil, Ş. İlker: An alternative globalization strategy for unconstrained optimization (2018)
  8. Andrei, Neculai: Accelerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update (2017)
  9. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  10. Ataee Tarzanagh, D.; Nazari, P.; Reza Peyghami, M.: A nonmonotone PRP conjugate gradient method for solving square and under-determined systems of equations (2017)
  11. Babaie-Kafaki, Saman; Ghanbari, Reza: A class of adaptive dai-liao conjugate gradient methods based on the scaled memoryless BFGS update (2017)
  12. 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)
  13. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  14. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  15. Cano, Javier; Moguerza, Javier M.; Prieto, Francisco J.: Using improved directions of negative curvature for the solution of bound-constrained nonconvex problems (2017)
  16. Dong, Xiao Liang; Li, Wei Jun; He, Yu Bo: Some modified Yabe-Takano conjugate gradient methods with sufficient descent condition (2017)
  17. Fang, Xiaowei; Ni, Qin: A frame-based conjugate gradients direct search method with radial basis function interpolation model (2017)
  18. Gao, Huan; Zhang, Hai-Bin; Li, Zhi-Bao; Tadjouddine, Emmanuel: A nonmonotone inexact Newton method for unconstrained optimization (2017)
  19. Huang, Yuanyuan; Liu, Changhe: Dai-Kou type conjugate gradient methods with a line search only using gradient (2017)
  20. Li, Xiangrong; Wang, Bopeng; Hu, Wujie: A modified nonmonotone BFGS algorithm for unconstrained optimization (2017)

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