SifDec

CUTEr and SifDec: a constrained and unconstrained testing environment, revisited The initial release of CUTE, a widely used testing environment for optimization software, was described by {it I. Bongartz}, et al. [ibid. 21, No. 1, 123--160 (1995; Zbl 0886.65058)]. A new version, now known as CUTEr, is presented. Features include reorganisation of the environment to allow simultaneous multi-platform installation, new tools for, and interfaces to, optimization packages, and a considerably simplified and entirely automated installation procedure for unix systems. The environment is fully backward compatible with its predecessor, and offers support for Fortran 90/95 and a general C/C++ Application Programming Interface. The SIF decoder, formerly a part of CUTE, has become a separate tool, easily callable by various packages. It features simple extensions to the SIF test problem format and the generation of files suited to automatic differentiation packages.


References in zbMATH (referenced in 194 articles , 1 standard article )

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  1. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A shifted primal-dual penalty-barrier method for nonlinear optimization (2020)
  2. Liu, Zexian; Liu, Hongwei; Dai, Yu-Hong: An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization (2020)
  3. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  4. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  5. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  6. Huang, Na; Ma, Chang-Feng: Spectral analysis of the preconditioned system for the (3 \times3) block saddle point problem (2019)
  7. Jiang, Xianzhen; Jian, Jinbao: Improved Fletcher-Reeves and Dai-Yuan conjugate gradient methods with the strong Wolfe line search (2019)
  8. Liu, Hongwei; Liu, Zexian: An efficient Barzilai-Borwein conjugate gradient method for unconstrained optimization (2019)
  9. Liu, Zexian; Liu, Hongwei: An efficient gradient method with approximately optimal stepsize based on tensor model for unconstrained optimization (2019)
  10. Li, Yufei; Liu, Zexian; Liu, Hongwei: A subspace minimization conjugate gradient method based on conic model for unconstrained optimization (2019)
  11. Petra, Cosmin G.; Chiang, Naiyuan; Anitescu, Mihai: A structured quasi-Newton algorithm for optimizing with incomplete Hessian information (2019)
  12. Xue, Yanqin; Liu, Hongwei; Liu, Zexian: An improved nonmonotone adaptive trust region method. (2019)
  13. Zhang, Keke; Liu, Hongwei; Liu, Zexian: A new Dai-Liao conjugate gradient method with optimal parameter choice (2019)
  14. 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)
  15. Audet, Charles; Tribes, Christophe: Mesh-based Nelder-Mead algorithm for inequality constrained optimization (2018)
  16. Huyer, Waltraud; Neumaier, Arnold: MINQ8: general definite and bound constrained indefinite quadratic programming (2018)
  17. Nedělková, Zuzana; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith: Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space (2018)
  18. Öztoprak, Figen; Birbil, Ş. İlker: An alternative globalization strategy for unconstrained optimization (2018)
  19. Yao, Shengwei; He, Donglei; Shi, Lihua: An improved Perry conjugate gradient method with adaptive parameter choice (2018)
  20. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)

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