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 206 articles , 1 standard article )

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  1. Zhang, Li: Two modified Dai-Yuan nonlinear conjugate gradient methods (2009)
  2. Andreani, R.; Birgin, E. G.; Martínez, J. M.; Schuverdt, M. L.: Augmented Lagrangian methods under the constant positive linear dependence constraint qualification (2008)
  3. Andrei, Neculai: Another hybrid conjugate gradient algorithm for unconstrained optimization (2008)
  4. Andrei, Neculai: A Dai-Yuan conjugate gradient algorithm with sufficient descent and conjugacy conditions for unconstrained optimization (2008)
  5. Birgin, E. G.; Martínez, J. M.: Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization (2008)
  6. Hager, William W.; Zhang, Hongchao: Self-adaptive inexact proximal point methods (2008)
  7. Ibrahim, Walid; Chinneck, John W.: Improving solver success in reaching feasibility for sets of nonlinear constraints (2008)
  8. Marcilio, Débora Cintia; Matioli, Luiz Carlos: Augmented Lagrangian applied to convex quadratic problems (2008)
  9. Matioli, L. C.; Gonzaga, C. C.: A new family of penalties for augmented Lagrangian methods (2008)
  10. Olivares, Alberto; Moguerza, Javier M.; Prieto, Francisco J.: Nonconvex optimization using negative curvature within a modified linesearch (2008)
  11. Wang, Haijun; Ni, Qin: A new method of moving asymptotes for large-scale unconstrained optimization (2008)
  12. Zhang, Li; Zhou, Weijun: Two descent hybrid conjugate gradient methods for optimization (2008)
  13. Andrei, Neculai: A scaled BFGS preconditioned conjugate gradient algorithm for unconstrained optimization (2007)
  14. Andrei, Neculai: Scaled conjugate gradient algorithms for unconstrained optimization (2007)
  15. Pytlak, R.; Tarnawski, T.: On the method of shortest residuals for unconstrained optimization (2007)
  16. Schenk, Olaf; Wächter, Andreas; Hagemann, Michael: Matching-based preprocessing algorithms to the solution of saddle-point problems in large-scale nonconvex interior-point optimization (2007)
  17. Wah, Benjamin W.; Chen, Yixin; Wang, Tao: Simulated annealing with asymptotic convergence for nonlinear constrained optimization (2007)
  18. Zhang, Juliang; Wu, Lingyun; Zhang, Xiangsun: A trust region method for optimization problem with singular solutions (2007)
  19. Andrei, Neculai: An acceleration of gradient descent algorithm with backtracking for unconstrained optimization (2006)
  20. Chen, L. H.; Deng, N. Y.; Zhang, J. Z.: A modified quasi-Newton method for structured optimization with partial information on the Hessian (2006)

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