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 126 articles )

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  1. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  2. Gibali, Aviv; Küfer, Karl-Heinz; Reem, Daniel; Süss, Philipp: A generalized projection-based scheme for solving convex constrained optimization problems (2018)
  3. Kuno, Takahito; Sano, Yoshio; Tsuruda, Takahiro: Computing Kitahara-Mizuno’s bound on the number of basic feasible solutions generated with the simplex algorithm (2018)
  4. Lungten, Sangye; Schilders, Wil H. A.; Maubach, Joseph M. L.: Threshold incomplete factorization constraint preconditioners for saddle-point matrices (2018)
  5. Alli-Oke, Razak O.; Heath, William P.: A secant-based Nesterov method for convex functions (2017)
  6. Gould, Nicholas I. M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  7. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  8. Kheirfam, Behrouz: An infeasible full-NT step interior point algorithm for CQSCO (2017)
  9. Morini, Benedetta; Simoncini, Valeria; Tani, Mattia: A comparison of reduced and unreduced KKT systems arising from interior point methods (2017)
  10. Hager, William W.; Zhang, Hongchao: Projection onto a polyhedron that exploits sparsity (2016)
  11. Morini, Benedetta; Simoncini, Valeria; Tani, Mattia: Spectral estimates for unreduced symmetric KKT systems arising from interior point methods. (2016)
  12. Curtis, Frank E.; Han, Zheng; Robinson, Daniel P.: A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization (2015)
  13. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  14. Ma, Ding; Saunders, Michael A.: Solving multiscale linear programs using the simplex method in quadruple precision (2015)
  15. Orban, Dominique: Limited-memory LDL(^\top) factorization of symmetric quasi-definite matrices with application to constrained optimization (2015)
  16. Tian, Da Gang: An exterior point polynomial-time algorithm for convex quadratic programming (2015)
  17. Chen, Fei; Xiang, Tao; Yang, Yuanyuan: Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud (2014)
  18. Ferreau, Hans Joachim; Kirches, Christian; Potschka, Andreas; Bock, Hans Georg; Diehl, Moritz: qpOASES: a parametric active-set algorithm for quadratic programming (2014)
  19. Winternitz, Luke B.; Tits, André L.; Absil, P.-A.: Addressing rank degeneracy in constraint-reduced interior-point methods for linear optimization (2014)
  20. Gould, Nicholas I. M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)

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