CUTE

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 188 articles , 1 standard article )

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  1. Andrei, Neculai: An acceleration of gradient descent algorithm with backtracking for unconstrained optimization (2006)
  2. Chen, L. H.; Deng, N. Y.; Zhang, J. Z.: A modified quasi-Newton method for structured optimization with partial information on the Hessian (2006)
  3. Hager, William W.; Zhang, Hongchao: Algorithm 851: CG_DESCENT, a conjugate gradient method with guaranteed descent. (2006)
  4. Shi, Zhen-Jun; Shen, Jie: Convergence of nonmonotone line search method (2006)
  5. Waltz, R. A.; Morales, J. L.; Nocedal, J.; Orban, D.: An interior algorithm for nonlinear optimization that combines line search and trust region steps (2006)
  6. Wang, Zhou-Hong; Yuan, Ya-Xiang: A subspace implementation of quasi-Newton trust region methods for unconstrained optimization (2006)
  7. Zhang, Li; Zhou, Weijun; Li, Donghui: Global convergence of a modified Fletcher-Reeves conjugate gradient method with Armijo-type line search (2006)
  8. Zhou, Bin; Gao, Li; Dai, Yuhong: Monotone projected gradient methods for large-scale box-constrained quadratic programming (2006)
  9. Herskovits, J.; Mappa, P.; Goulart, E.; Mota Soares, C. M.: Mathematical programming models and algorithms for engineering design optimization (2005)
  10. Yamashita, Hiroshi; Yabe, Hiroshi; Tanabe, Takahito: A globally and superlinearly convergent primal-dual interior point trust region method for large scale constrained optimization (2005)
  11. Diniz-Ehrhardt, M. A.; Gomes-Ruggiero, M. A.; Martínez, J. M.; Santos, S. A.: Augmented Lagrangian algorithms based on the spectral projected gradient method for solving nonlinear programming problems (2004)
  12. Gertz, M.; Nocedal, J.; Sartenaer, A.: A starting point strategy for nonlinear interior methods. (2004)
  13. Forsgren, Anders: Inertia-controlling factorizations for optimization algorithms (2002)
  14. Maros, István; Khaliq, Mohammad Haroon: Advances in design and implementation of optimization software (2002)
  15. Morales, J. L.: A numerical study of limited memory BFGS methods (2002)
  16. Diniz-Ehrhardt, M. A.; Dostál, Z.; Gomes-Ruggiero, M. A.; Martínez, J. M.; Santos, S. A.: Nonmonotone strategy for minimization of quadratics with simple constraints. (2001)
  17. Kappel, Franz; Kuntsevich, Alexei V.: An implementation of Shor’s (r)-algorithm (2000)
  18. Holmström, Kenneth: The TOMLAB optimization environment in MATLAB (1999)
  19. Maros, István; Mészáros, Csaba: A repository of convex quadratic programming problems (1999)
  20. Wang, X.; Chang, T. S.: A framework for globally convergent algorithms using gradient bounding functions (1999)

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