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 117 articles )

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  1. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  2. Kheirfam, Behrouz: An infeasible full-NT step interior point algorithm for CQSCO (2017)
  3. Morini, Benedetta; Simoncini, Valeria; Tani, Mattia: A comparison of reduced and unreduced KKT systems arising from interior point methods (2017)
  4. Hager, William W.; Zhang, Hongchao: Projection onto a polyhedron that exploits sparsity (2016)
  5. Curtis, Frank E.; Han, Zheng; Robinson, Daniel P.: A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization (2015)
  6. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  7. Ma, Ding; Saunders, Michael A.: Solving multiscale linear programs using the simplex method in quadruple precision (2015)
  8. Orban, Dominique: Limited-memory LDL$^\top$ factorization of symmetric quasi-definite matrices with application to constrained optimization (2015)
  9. Tian, Da Gang: An exterior point polynomial-time algorithm for convex quadratic programming (2015)
  10. Chen, Fei; Xiang, Tao; Yang, Yuanyuan: Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud (2014)
  11. Ferreau, Hans Joachim; Kirches, Christian; Potschka, Andreas; Bock, Hans Georg; Diehl, Moritz: qpOASES: a parametric active-set algorithm for quadratic programming (2014)
  12. Winternitz, Luke B.; Tits, André L.; Absil, P.-A.: Addressing rank degeneracy in constraint-reduced interior-point methods for linear optimization (2014)
  13. Gould, Nicholas I.M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)
  14. Khorramizadeh, M.; Mahdavi-Amiri, N.: An efficient algorithm for sparse null space basis problem using ABS methods (2013)
  15. Li, Wei: Dual-primal algorithm for linear optimization (2013)
  16. Pan, Ping-Qi: An affine-scaling pivot algorithm for linear programming (2013)
  17. Althaus, Ernst; Dumitriu, Daniel: Certifying feasibility and objective value of linear programs (2012)
  18. Bentobache, Mohand; Bibi, Mohand Ouamer: A two-phase support method for solving linear programs: numerical experiments (2012)
  19. Friedlander, M.P.; Orban, D.: A primal-dual regularized interior-point method for convex quadratic programs (2012)
  20. Gleixner, Ambros M.; Steffy, Daniel E.; Wolter, Kati: Improving the accuracy of linear programming solvers with iterative refinement (2012)

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