PCx: An interior-point code for linear programming We describe the code PCx, a primal-dual interior-point code for linear programming. Information is given about problem formulation and the underlying algorithm, along with instructions for installing, invoking, and using the code. Computational results on standard test problems are reported.

References in zbMATH (referenced in 40 articles , 1 standard article )

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  1. Velazco, Marta; Oliveira, Aurelio R. L.: Computing the splitting preconditioner for interior point method using an incomplete factorization approach (2018)
  2. Yang, Y.: Two computationally efficient polynomial-iteration infeasible interior-point algorithms for linear programming (2018)
  3. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  4. Yang, Yaguang: CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming (2017)
  5. Asadi, Alireza; Roos, Cornelis: Infeasible interior-point methods for linear optimization based on large neighborhood (2016)
  6. Bougnol, Marie-Laure; Dulá, Jose H.: The other side of ranking schemes: generating weights for specified outcomes (2016)
  7. Barbara, Abdessamad: Strict quasi-concavity and the differential barrier property of gauges in linear programming (2015)
  8. Bougnol, Marie-Laure; Dulá, José H.; Rouse, Paul: Interior point methods in DEA to determine non-zero multiplier weights (2012)
  9. Friedlander, M. P.; Orban, D.: A primal-dual regularized interior-point method for convex quadratic programs (2012)
  10. Ghidini, Carla T. L. S.; Oliveira, A. R. L.; Silva, Jair.; Velazco, M. I.: Combining a hybrid preconditioner and a optimal adjustment algorithm to accelerate the convergence of interior point methods (2012)
  11. Lubin, Miles; Petra, Cosmin G.; Anitescu, Mihai: The parallel solution of dense saddle-point linear systems arising in stochastic programming (2012)
  12. Mehrotra, Sanjay; Huang, Kuo-Ling: Computational experience with a modified potential reduction algorithm for linear programming (2012)
  13. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  14. Zhang, Mingwang: A second order Mehrotra-type predictor-corrector algorithm for semidefinite optimization (2012)
  15. Baena, Daniel; Castro, Jordi: Using the analytic center in the feasibility pump (2011)
  16. Castro, Jordi; Cuesta, Jordi: Quadratic regularizations in an interior-point method for primal block-angular problems (2011)
  17. Velazco, M. I.; Oliveira, A. R. L.; Campos, F. F.: A note on hybrid preconditioners for large-scale normal equations arising from interior-point methods (2010)
  18. Pietrabissa, Antonio: Optimal call admission and call dropping control in links with variable capacity (2009)
  19. Colombo, Marco; Gondzio, Jacek: Further development of multiple centrality correctors for interior point methods (2008)
  20. John, Elizabeth; Yıldırım, E. Alper: Implementation of warm-start strategies in interior-point methods for linear programming in fixed dimension (2008)

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