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 582 articles , 1 standard article )

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  1. Abdollahi, Fahimeh; Fatemi, Masoud: A new conjugate gradient method based on a modified secant condition with its applications in image processing (2021)
  2. Aminifard, Zohre; Babaie-Kafaki, Saman; Ghafoori, Saeide: An augmented memoryless BFGS method based on a modified secant equation with application to compressed sensing (2021)
  3. Audet, Charles; Bigeon, Jean; Couderc, Romain: Combining cross-entropy and MADS methods for inequality constrained global optimization (2021)
  4. Berahas, Albert S.; Curtis, Frank E.; Robinson, Daniel; Zhou, Baoyu: Sequential quadratic optimization for nonlinear equality constrained stochastic optimization (2021)
  5. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  6. Dehghani, R.; Bidabadi, N.; Hosseini, M. M.: Using nonlinear functions to approximate a new quasi-Newton method for unconstrained optimization problems (2021)
  7. Faramarzi, Parvaneh; Amini, Keyvan: A spectral three-term Hestenes-Stiefel conjugate gradient method (2021)
  8. Gonçalves, Douglas S.; Gonçalves, Max L. N.; Oliveira, Fabrícia R.: An inexact projected LM type algorithm for solving convex constrained nonlinear equations (2021)
  9. 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)
  10. Leong, Wah June; Enshaei, Sharareh; Kek, Sie Long: Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm (2021)
  11. Zhao, Ting; Liu, Hongwei; Liu, Zexian: New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization (2021)
  12. Aminifard, Zohre; Babaie-Kafaki, Saman: Modified spectral conjugate gradient methods based on the quasi-Newton aspects (2020)
  13. Andrei, Neculai: Diagonal approximation of the Hessian by finite differences for unconstrained optimization (2020)
  14. Andrei, Neculai: A double parameter self-scaling memoryless BFGS method for unconstrained optimization (2020)
  15. Andrei, Neculai: New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (2020)
  16. Babaie-Kafaki, Saman: A modified scaled memoryless symmetric rank-one method (2020)
  17. Bartholomew-Biggs, Michael; Beddiaf, Salah; Christianson, Bruce: A comparison of methods for traversing regions of non-convexity in optimization problems (2020)
  18. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.: Computationally efficient decompositions of oblique projection matrices (2020)
  19. Dai, Yu-Hong; Liu, Xin-Wei; Sun, Jie: A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs (2020)
  20. Dehghani, Razieh; Bidabadi, Narges; Fahs, Hassan; Hosseini, Mohammad Mehdi: A conjugate gradient method based on a modified secant relation for unconstrained optimization (2020)

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