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 569 articles , 1 standard article )

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  1. Gonçalves, Douglas S.; Gonçalves, Max L. N.; Oliveira, Fabrícia R.: An inexact projected LM type algorithm for solving convex constrained nonlinear equations (2021)
  2. Ivanov, Branislav; Stanimirović, Predrag S.; Shaini, Bilall I.; Ahmad, Hijaz; Wang, Miao-Kun: A novel value for the parameter in the Dai-Liao-type conjugate gradient method (2021)
  3. Leong, Wah June; Enshaei, Sharareh; Kek, Sie Long: Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm (2021)
  4. Andrei, Neculai: A double parameter self-scaling memoryless BFGS method for unconstrained optimization (2020)
  5. Andrei, Neculai: New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (2020)
  6. Andrei, Neculai: Diagonal approximation of the Hessian by finite differences for unconstrained optimization (2020)
  7. Babaie-Kafaki, Saman: A modified scaled memoryless symmetric rank-one method (2020)
  8. Bartholomew-Biggs, Michael; Beddiaf, Salah; Christianson, Bruce: A comparison of methods for traversing regions of non-convexity in optimization problems (2020)
  9. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.: Computationally efficient decompositions of oblique projection matrices (2020)
  10. Dai, Yu-Hong; Liu, Xin-Wei; Sun, Jie: A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs (2020)
  11. Dehghani, Razieh; Bidabadi, Narges; Fahs, Hassan; Hosseini, Mohammad Mehdi: A conjugate gradient method based on a modified secant relation for unconstrained optimization (2020)
  12. Diao, Xinliu; Liu, Hongwei; Liu, Zexian: A new subspace minimization conjugate gradient method based on modified secant equation for unconstrained optimization (2020)
  13. Estrin, Ron; Friedlander, Michael P.; Orban, Dominique; Saunders, Michael A.: Implementing a smooth exact penalty function for general constrained nonlinear optimization (2020)
  14. Faramarzi, Parvaneh; Amini, Keyvan: A modified conjugate gradient method based on a modified secant equation (2020)
  15. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A shifted primal-dual penalty-barrier method for nonlinear optimization (2020)
  16. Huang, Na: Variable parameter Uzawa method for solving a class of block three-by-three saddle point problems (2020)
  17. Li, Min: A three term Polak-Ribière-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method (2020)
  18. Liu, J. K.; Zhao, Y. X.; Wu, X. L.: Some three-term conjugate gradient methods with the new direction structure (2020)
  19. Liu, Meixing; Ma, Guodong; Yin, Jianghua: Two new conjugate gradient methods for unconstrained optimization (2020)
  20. Liu, Xin-Wei; Dai, Yu-Hong: A globally convergent primal-dual interior-point relaxation method for nonlinear programs (2020)

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