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 507 articles , 1 standard article )

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  1. Jiang, Xianzhen; Jian, Jinbao: Improved Fletcher-Reeves and Dai-Yuan conjugate gradient methods with the strong Wolfe line search (2019)
  2. Liu, Hongwei; Liu, Zexian: An efficient Barzilai-Borwein conjugate gradient method for unconstrained optimization (2019)
  3. Petra, Cosmin G.; Chiang, Naiyuan; Anitescu, Mihai: A structured quasi-Newton algorithm for optimizing with incomplete Hessian information (2019)
  4. Rezaee, Saeed; Babaie-Kafaki, Saman: An adaptive nonmonotone trust region algorithm (2019)
  5. Vlček, Jan; Lukšan, Ladislav: A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions (2019)
  6. Vlček, Jan; Lukšan, Ladislav: Properties of the block BFGS update and its application to the limited-memory block BNS method for unconstrained minimization (2019)
  7. Zhang, Keke; Liu, Hongwei; Liu, Zexian: A new Dai-Liao conjugate gradient method with optimal parameter choice (2019)
  8. Zhou, W.; Akrotirianakis, I. G.; Yektamaram, S.; Griffin, J. D.: A matrix-free line-search algorithm for nonconvex optimization (2019)
  9. Ali, M. Montaz; Oliphant, Terry-Leigh: A trajectory-based method for constrained nonlinear optimization problems (2018)
  10. 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)
  11. Andrei, Neculai: A Dai-Liao conjugate gradient algorithm with clustering of eigenvalues (2018)
  12. Andrei, Neculai: A double parameter scaled BFGS method for unconstrained optimization (2018)
  13. Audet, Charles; Tribes, Christophe: Mesh-based Nelder-Mead algorithm for inequality constrained optimization (2018)
  14. Babaie-Kafaki, Saman; Ghanbari, Reza: Two adaptive Dai-Liao nonlinear conjugate gradient methods (2018)
  15. Babaie-Kafaki, Saman; Ghanbari, Reza: A linear hybridization of the Hestenes-Stiefel method and the memoryless BFGS technique (2018)
  16. Babaie-Kafaki, Saman; Rezaee, Saeed: Two accelerated nonmonotone adaptive trust region line search methods (2018)
  17. Benson, Hande Y.; Shanno, David F.: Cubic regularization in symmetric rank-1 quasi-Newton methods (2018)
  18. Dehghani, Razieh; Bidabadi, Narges; Hosseini, Mohammad Mehdi: A new modified BFGS method for unconstrained optimization problems (2018)
  19. Dong, XiaoLiang; Han, Deren; Dai, Zhifeng; Li, Lixiang; Zhu, Jianguang: An accelerated three-term conjugate gradient method with sufficient descent condition and conjugacy condition (2018)
  20. Huyer, Waltraud; Neumaier, Arnold: MINQ8: general definite and bound constrained indefinite quadratic programming (2018)

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