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 162 articles , 1 standard article )

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  1. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  2. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  3. Fang, Xiaowei; Ni, Qin: A frame-based conjugate gradients direct search method with radial basis function interpolation model (2017)
  4. Gao, Huan; Zhang, Hai-Bin; Li, Zhi-Bao; Tadjouddine, Emmanuel: A nonmonotone inexact Newton method for unconstrained optimization (2017)
  5. Huang, Yuanyuan; Liu, Changhe: Dai-Kou type conjugate gradient methods with a line search only using gradient (2017)
  6. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  7. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  8. Zhou, Guanghui; Ni, Qin; Zeng, Meilan: A scaled conjugate gradient method with moving asymptotes for unconstrained optimization problems (2017)
  9. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  10. Du, Xuewu; Zhang, Peng; Ma, Wenya: Some modified conjugate gradient methods for unconstrained optimization (2016)
  11. Fatemi, M.: An optimal parameter for Dai-Liao family of conjugate gradient methods (2016)
  12. Fatemi, Masoud: A new efficient conjugate gradient method for unconstrained optimization (2016)
  13. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  14. Garmanjani, R.; Júdice, D.; Vicente, L.N.: Trust-region methods without using derivatives: worst case complexity and the nonsmooth case (2016)
  15. Shen, Chungen; Zhang, Lei-Hong; Liu, Wei: A stabilized filter SQP algorithm for nonlinear programming (2016)
  16. Zhu, Xiaojing: On a globally convergent trust region algorithm with infeasibility control for equality constrained optimization (2016)
  17. Al-Baali, Mehiddin; Narushima, Yasushi; Yabe, Hiroshi: A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization (2015)
  18. Bianconcini, Tommaso; Liuzzi, Giampaolo; Morini, Benedetta; Sciandrone, Marco: On the use of iterative methods in cubic regularization for unconstrained optimization (2015)
  19. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  20. Curtis, Frank E.; Jiang, Hao; Robinson, Daniel P.: An adaptive augmented Lagrangian method for large-scale constrained optimization (2015)

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