NETLIB LP Test Set

The NETLIB LP Test Problem Set. The NETLIB Linear Programming test set is a collection of real-life linear programming examples from a variety of sources. The examples are available in MPS format, which is a subset of the SIF format used by CUTEr. Thus, the NETLIB set provide a further collection of interesting examples for those who have CUTEr interfaces to their optimization packages.


References in zbMATH (referenced in 137 articles )

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  1. De Marchi, Alberto: On a primal-dual Newton proximal method for convex quadratic programs (2022)
  2. Zhang, Mingwang; Huang, Kun; Lv, Yanli: A wide neighborhood arc-search interior-point algorithm for convex quadratic programming with box constraints and linear constraints (2022)
  3. Bartmeyer, Petra Maria; Bocanegra, Silvana; Oliveira, Aurelio Ribeiro Leite: Switching preconditioners using a hybrid approach for linear systems arising from interior point methods for linear programming (2021)
  4. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  5. O’Donoghue, Brendan: Operator splitting for a homogeneous embedding of the linear complementarity problem (2021)
  6. Pougkakiotis, Spyridon; Gondzio, Jacek: An interior point-proximal method of multipliers for convex quadratic programming (2021)
  7. Borgwardt, Steffen; Viss, Charles: An implementation of steepest-descent augmentation for linear programs (2020)
  8. Shen, Chungen; Xue, Wenjuan; Zhang, Lei-Hong; Wang, Baiyun: An active-set proximal-Newton algorithm for (\ell_1) regularized optimization problems with box constraints (2020)
  9. Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
  10. Pougkakiotis, Spyridon; Gondzio, Jacek: Dynamic non-diagonal regularization in interior point methods for linear and convex quadratic programming (2019)
  11. Weber, Tobias; Sager, Sebastian; Gleixner, Ambros: Solving quadratic programs to high precision using scaled iterative refinement (2019)
  12. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  13. Gibali, Aviv; Küfer, Karl-Heinz; Reem, Daniel; Süss, Philipp: A generalized projection-based scheme for solving convex constrained optimization problems (2018)
  14. Kuno, Takahito; Sano, Yoshio; Tsuruda, Takahiro: Computing Kitahara-Mizuno’s bound on the number of basic feasible solutions generated with the simplex algorithm (2018)
  15. Lungten, Sangye; Schilders, Wil H. A.; Maubach, Joseph M. L.: Threshold incomplete factorization constraint preconditioners for saddle-point matrices (2018)
  16. Alli-Oke, Razak O.; Heath, William P.: A secant-based Nesterov method for convex functions (2017)
  17. Gould, Nicholas I. M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  18. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  19. Kheirfam, Behrouz: An infeasible full-NT step interior point algorithm for CQSCO (2017)
  20. Morini, Benedetta; Simoncini, Valeria; Tani, Mattia: A comparison of reduced and unreduced KKT systems arising from interior point methods (2017)

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