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.

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  3. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
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  7. Du, Xuewu; Zhang, Peng; Ma, Wenya: Some modified conjugate gradient methods for unconstrained optimization (2016)
  8. Fatemi, M.: An optimal parameter for Dai-Liao family of conjugate gradient methods (2016)
  9. Fatemi, Masoud: A new efficient conjugate gradient method for unconstrained optimization (2016)
  10. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  11. Garmanjani, R.; Júdice, D.; Vicente, L.N.: Trust-region methods without using derivatives: worst case complexity and the nonsmooth case (2016)
  12. Shen, Chungen; Zhang, Lei-Hong; Liu, Wei: A stabilized filter SQP algorithm for nonlinear programming (2016)
  13. Zhu, Xiaojing: On a globally convergent trust region algorithm with infeasibility control for equality constrained optimization (2016)
  14. Al-Baali, Mehiddin; Narushima, Yasushi; Yabe, Hiroshi: A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization (2015)
  15. Bianconcini, Tommaso; Liuzzi, Giampaolo; Morini, Benedetta; Sciandrone, Marco: On the use of iterative methods in cubic regularization for unconstrained optimization (2015)
  16. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  17. Curtis, Frank E.; Jiang, Hao; Robinson, Daniel P.: An adaptive augmented Lagrangian method for large-scale constrained optimization (2015)
  18. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  19. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  20. Huang, Shuai; Wan, Zhong; Chen, Xiaohong: A new nonmonotone line search technique for unconstrained optimization (2015)

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