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 112 articles , 1 standard article )

Showing results 1 to 20 of 112.
Sorted by year (citations)

1 2 3 4 5 6 next

  1. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  2. Yamashita, Makoto; Iida, Einosuke; Yang, Yaguang: An infeasible interior-point arc-search algorithm for nonlinear constrained optimization (2022)
  3. 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
  4. Audet, Charles; Dzahini, Kwassi Joseph; Kokkolaras, Michael; Le Digabel, Sébastien: Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates (2021)
  5. Birgin, E. G.; Gardenghi, J. L.; Martínez, J. M.; Santos, S. A.: On the solution of linearly constrained optimization problems by means of barrier algorithms (2021)
  6. Boutet, Nicolas; Haelterman, Rob; Degroote, Joris: Secant update generalized version of PSB: a new approach (2021)
  7. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  8. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  9. 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)
  10. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  11. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  12. Ek, David; Forsgren, Anders: Exact linesearch limited-memory quasi-Newton methods for minimizing a quadratic function (2021)
  13. Huang, Ya-Kui; Dai, Yu-Hong; Liu, Xin-Wei: Equipping the Barzilai-Borwein method with the two dimensional quadratic termination property (2021)
  14. Jiang, Rujun; Yue, Man-Chung; Zhou, Zhishuo: An accelerated first-order method with complexity analysis for solving cubic regularization subproblems (2021)
  15. Kimiaei, Morteza; Esmaeili, Hamid; Rahpeymaii, Farzad: A trust-region method using extended nonmonotone technique for unconstrained optimization (2021)
  16. Ahmadvand, M.; Esmaeilbeigi, M.; Yaghoobi, F.; Kamandi, A.: Performance evaluation of ORBIT algorithm to some effective parameters (2020)
  17. 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)
  18. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  19. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  20. 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)

1 2 3 4 5 6 next