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 152 articles , 1 standard article )

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  1. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  2. Du, Xuewu; Zhang, Peng; Ma, Wenya: Some modified conjugate gradient methods for unconstrained optimization (2016)
  3. Fatemi, M.: An optimal parameter for dai-liao family of conjugate gradient methods (2016)
  4. Fatemi, Masoud: A new efficient conjugate gradient method for unconstrained optimization (2016)
  5. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
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  7. Shen, Chungen; Zhang, Lei-Hong; Liu, Wei: A stabilized filter SQP algorithm for nonlinear programming (2016)
  8. Zhu, Xiaojing: On a globally convergent trust region algorithm with infeasibility control for equality constrained optimization (2016)
  9. Al-Baali, Mehiddin; Narushima, Yasushi; Yabe, Hiroshi: A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization (2015)
  10. Bianconcini, Tommaso; Liuzzi, Giampaolo; Morini, Benedetta; Sciandrone, Marco: On the use of iterative methods in cubic regularization for unconstrained optimization (2015)
  11. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  12. Curtis, Frank E.; Jiang, Hao; Robinson, Daniel P.: An adaptive augmented Lagrangian method for large-scale constrained optimization (2015)
  13. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  14. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  15. Huang, Shuai; Wan, Zhong; Chen, Xiaohong: A new nonmonotone line search technique for unconstrained optimization (2015)
  16. Hungerländer, P.; Rendl, F.: A feasible active set method for strictly convex quadratic problems with simple bounds (2015)
  17. Jian, Jin-Bao; Guo, Chuan-Hao; Tang, Chun-Ming; Bai, Yan-Qin: A new superlinearly convergent algorithm of combining QP subproblem with system of linear equations for nonlinear optimization (2015)
  18. Kou, C.X.; Dai, Y.H.: A modified self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method for unconstrained optimization (2015)
  19. Li, Wenyu; Yang, Yueting: A nonmonotone hybrid conjugate gradient method for unconstrained optimization (2015)
  20. Orban, Dominique: Limited-memory LDL$^\top$ factorization of symmetric quasi-definite matrices with application to constrained optimization (2015)

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