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 209 articles , 1 standard article )

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  1. Audet, Charles; Bigeon, Jean; Couderc, Romain: Combining cross-entropy and MADS methods for inequality constrained global optimization (2021)
  2. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  3. Zhao, Ting; Liu, Hongwei; Liu, Zexian: New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization (2021)
  4. Bartholomew-Biggs, Michael; Beddiaf, Salah; Christianson, Bruce: A comparison of methods for traversing regions of non-convexity in optimization problems (2020)
  5. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.: Computationally efficient decompositions of oblique projection matrices (2020)
  6. Diao, Xinliu; Liu, Hongwei; Liu, Zexian: A new subspace minimization conjugate gradient method based on modified secant equation for unconstrained optimization (2020)
  7. Estrin, Ron; Friedlander, Michael P.; Orban, Dominique; Saunders, Michael A.: Implementing a smooth exact penalty function for general constrained nonlinear optimization (2020)
  8. Faramarzi, Parvaneh; Amini, Keyvan: A modified conjugate gradient method based on a modified secant equation (2020)
  9. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A shifted primal-dual penalty-barrier method for nonlinear optimization (2020)
  10. Huang, Na: Variable parameter Uzawa method for solving a class of block three-by-three saddle point problems (2020)
  11. Liu, Zexian; Liu, Hongwei; Dai, Yu-Hong: An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization (2020)
  12. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  13. Xia, Wei; Vera, Juan C.; Zuluaga, Luis F.: Globally solving nonconvex quadratic programs via linear integer programming techniques (2020)
  14. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  15. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  16. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  17. Huang, Na; Ma, Chang-Feng: Spectral analysis of the preconditioned system for the (3 \times3) block saddle point problem (2019)
  18. Jiang, Xianzhen; Jian, Jinbao: Improved Fletcher-Reeves and Dai-Yuan conjugate gradient methods with the strong Wolfe line search (2019)
  19. Liu, Hongwei; Liu, Zexian: An efficient Barzilai-Borwein conjugate gradient method for unconstrained optimization (2019)
  20. Liu, Zexian; Liu, Hongwei: An efficient gradient method with approximately optimal stepsize based on tensor model for unconstrained optimization (2019)

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