CUTEst: a constrained and unconstrained testing environment with safe threads. We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named SIFDecode and CUTEst , these updated versions feature dynamic memory allocation, a modern thread-safe Fortran modular design, a new Matlab interface and a revised installation procedure integrated with GALAHAD.

References in zbMATH (referenced in 107 articles , 1 standard article )

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  1. Anders Markvardsen, Tyrone Rees, Michael Wathen, Andrew Lister, Patrick Odagiu, Atijit Anuchitanukul, Tom Farmer, Anthony Lim, Federico Montesino, Tim Snow, Andrew McCluskey: FitBenchmarking: an open source Python package comparing data fitting software (2021) not zbMATH
  2. Audet, Charles; Dzahini, Kwassi Joseph; Kokkolaras, Michael; Le Digabel, Sébastien: Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates (2021)
  3. Boutet, Nicolas; Haelterman, Rob; Degroote, Joris: Secant update generalized version of PSB: a new approach (2021)
  4. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  5. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  6. Curtis, Frank E.; Robinson, Daniel P.; Royer, Clément W.; Wright, Stephen J.: Trust-region Newton-CG with strong second-order complexity guarantees for nonconvex optimization (2021)
  7. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  8. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  9. Ek, David; Forsgren, Anders: Exact linesearch limited-memory quasi-Newton methods for minimizing a quadratic function (2021)
  10. Jiang, Rujun; Yue, Man-Chung; Zhou, Zhishuo: An accelerated first-order method with complexity analysis for solving cubic regularization subproblems (2021)
  11. Ahmadvand, M.; Esmaeilbeigi, M.; Yaghoobi, F.; Kamandi, A.: Performance evaluation of ORBIT algorithm to some effective parameters (2020)
  12. Al-Baali, Mehiddin; Caliciotti, Andrea; Fasano, Giovanni; Roma, Massimo: A class of approximate inverse preconditioners based on Krylov-subspace methods for large-scale nonconvex optimization (2020)
  13. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  14. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  15. Caliciotti, Andrea; Fasano, Giovanni; Potra, Florian; Roma, Massimo: Issues on the use of a modified bunch and Kaufman decomposition for large scale Newton’s equation (2020)
  16. Dehghani, R.; Bidabadi, N.: Two-step conjugate gradient method for unconstrained optimization (2020)
  17. De Leone, Renato; Fasano, Giovanni; Roma, Massimo; Sergeyev, Yaroslav D.: Iterative grossone-based computation of negative curvature directions in large-scale optimization (2020)
  18. Estrin, Ron; Friedlander, Michael P.; Orban, Dominique; Saunders, Michael A.: Implementing a smooth exact penalty function for equality-constrained nonlinear optimization (2020)
  19. Gould, Nicholas I. M.; Simoncini, Valeria: Error estimates for iterative algorithms for minimizing regularized quadratic subproblems (2020)
  20. Gratton, S.; Royer, C. W.; Vicente, L. N.: A decoupled first/second-order steps technique for nonconvex nonlinear unconstrained optimization with improved complexity bounds (2020)

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