PCx

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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 45 articles , 1 standard article )

Showing results 1 to 20 of 45.
Sorted by year (citations)

1 2 3 next

  1. Heredia, Manolo Rodriguez; Oliveira, Aurelio Ribeiro Leite: A new proposal to improve the early iterations in the interior point method (2020)
  2. Asadi, Soodabeh; Mansouri, Hossein: A Mehrotra type predictor-corrector interior-point algorithm for linear programming (2019)
  3. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  4. Petra, Cosmin G.; Potra, Florian A.: A homogeneous model for monotone mixed horizontal linear complementarity problems (2019)
  5. Santos, Luiz-Rafael; Villas-Bôas, Fernando; Oliveira, Aurelio R. L.; Perin, Clovis: Optimized choice of parameters in interior-point methods for linear programming (2019)
  6. Velazco, Marta; Oliveira, Aurelio R. L.: Computing the splitting preconditioner for interior point method using an incomplete factorization approach (2018)
  7. Yang, Y.: Two computationally efficient polynomial-iteration infeasible interior-point algorithms for linear programming (2018)
  8. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  9. Yang, Yaguang: CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming (2017)
  10. Asadi, Alireza; Roos, Cornelis: Infeasible interior-point methods for linear optimization based on large neighborhood (2016)
  11. Bougnol, Marie-Laure; Dulá, Jose H.: The other side of ranking schemes: generating weights for specified outcomes (2016)
  12. Barbara, Abdessamad: Strict quasi-concavity and the differential barrier property of gauges in linear programming (2015)
  13. Bougnol, Marie-Laure; Dulá, José H.; Rouse, Paul: Interior point methods in DEA to determine non-zero multiplier weights (2012)
  14. Friedlander, M. P.; Orban, D.: A primal-dual regularized interior-point method for convex quadratic programs (2012)
  15. 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)
  16. Lubin, Miles; Petra, Cosmin G.; Anitescu, Mihai: The parallel solution of dense saddle-point linear systems arising in stochastic programming (2012)
  17. Mehrotra, Sanjay; Huang, Kuo-Ling: Computational experience with a modified potential reduction algorithm for linear programming (2012)
  18. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  19. Zhang, Mingwang: A second order Mehrotra-type predictor-corrector algorithm for semidefinite optimization (2012)
  20. Baena, Daniel; Castro, Jordi: Using the analytic center in the feasibility pump (2011)

1 2 3 next