CUTE: Constrained and unconstrained testing environment. The purpose of this article is to discuss the scope and functionality of a versatile environment for testing small- and large-scale nonlinear optimization algorithms. Although many of these facilities were originally produced by the authors in conjunction with the software package LANCELOT, we believe that they will be useful in their own right and should be available to researchers for their development of optimization software. The tools can be obtained by anonymous ftp from a number of sources and may, in many cases, be installed automatically. The scope of a major collection of test problems written in the standard input format (SIF) used by the LANCELOT software package is described. Recognizing that most software was not written with the SIF in mind, we provide tools to assist in building an interface between this input format and other optimization packages. These tools provide a link between the SIF and a number of existing packages, including MINOS and OSL. Additionally, as each problem includes a specific classification that is designed to be useful in identifying particular classes of problems, facilities are provided to build and manage a database of this information. There is a Unix and C shell bias to many of the descriptions in the article, since, for the sake of simplicity, we do not illustrate everything in its fullest generality. We trust that the majority of potential users are sufficiently familiar with Unix that these examples will not lead to undue confusion.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 148 articles , 1 standard article )

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  3. Gower, R.M.; Gower, A.L.: Higher-order reverse automatic differentiation with emphasis on the third-order (2016)
  4. Qiu, Songqiang; Chen, Zhongwen: A globally convergent penalty-free method for optimization with equality constraints and simple bounds (2016)
  5. Salleh, Zabidin; Alhawarat, Ahmad: An efficient modification of the Hestenes-Stiefel nonlinear conjugate gradient method with restart property (2016)
  6. Sellami, Badreddine; Chaib, Yacine: New conjugate gradient method for unconstrained optimization (2016)
  7. Shi, Zhanwen; Yang, Guanyu; Xiao, Yunhai: A limited memory BFGS algorithm for non-convex minimization with applications in matrix largest eigenvalue problem (2016)
  8. Wang, X.Y.; Li, S.J.; Kou, Xi Peng: A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints (2016)
  9. Yu, Zhensheng; Gan, Xinyue: Global and local R-linear convergence of a spectral projected gradient method for convex optimization with singular solution (2016)
  10. Al-Baali, Mehiddin; Narushima, Yasushi; Yabe, Hiroshi: A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization (2015)
  11. Chen, Yannan; Sun, Wenyu: A dwindling filter line search method for unconstrained optimization (2015)
  12. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  13. Liu, Hao; Shao, Jianfeng; Wang, Haijun; Chang, Baoxian: An adaptive sizing BFGS method for unconstrained optimization (2015)
  14. Liu, Hao; Wang, Haijun; Qian, Xiaoyan; Rao, Feng: A conjugate gradient method with sufficient descent property (2015)
  15. Livieris, Ioannis E.; Pintelas, Panagiotis: A modified Perry conjugate gradient method and its global convergence (2015)
  16. Yang, Yaguang: A globally and quadratically convergent algorithm with efficient implementation for unconstrained optimization (2015)
  17. Yuan, Gonglin (ed.); Yu, Gaohang (ed.); Andrei, Neculai (ed.); Xiao, Yunhai (ed.); Zhang, Li (ed.): Nonlinear analysis: optimization methods, convergence theory, and applications (2015)
  18. Zhou, Qunyan; Hang, Dan: Nonmonotone adaptive trust region method with line search based on new diagonal updating (2015)
  19. Zhou, Qunyan; Zhang, Chun: An adaptive trust region method based on simple conic models (2015)
  20. Benson, Hande Y.; Shanno, David F.: Interior-point methods for nonconvex nonlinear programming: cubic regularization (2014)

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