KORBX® (a registered trademark of AT&T) is AT&T’s new system for solving large-scale linear programs. The system consists of both hardware, which uses parallel processing technology configured with 256 MB of memory, and software which exploits the design and resources of this modern hardware. The KORBX linear programming software system contains four algorithms which are variations of the interior point method of Narendra Karmarkar. The primal, dual, primal-dual, and power series algorithms were empirically evaluated on a set of linear programming application models being used by the staff of the Military Airlift Command at Scott Air Force Base. For calibration purposes, a set of smaller test problems were also run using MPSX and XMP; and some pure network problems were solved using NETFLO, MPSX, and XMP.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 55 articles )

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  1. Glavelis, T.; Ploskas, N.; Samaras, N.: Improving a primal-dual simplex-type algorithm using interior point methods (2018)
  2. Suñagua, Porfirio; Oliveira, Aurelio R. L.: A new approach for finding a basis for the splitting preconditioner for linear systems from interior point methods (2017)
  3. Tar, Péter; Stágel, Bálint; Maros, István: Parallel search paths for the simplex algorithm (2017)
  4. Towhidi, Mehdi; Orban, Dominique: Customizing the solution process of COIN-OR’s linear solvers with python (2016)
  5. Huangfu, Qi; Hall, J. A. Julian: Novel update techniques for the revised simplex method (2015)
  6. Towhidi, Mehdi; Desrosiers, Jacques; Soumis, François: The positive edge criterion within COIN-OR’s CLP (2014)
  7. Bocanegra, Silvana; Castro, Jordi; Oliveira, Aurelio R. L.: Improving an interior-point approach for large block-angular problems by hybrid preconditioners (2013)
  8. Castro, Jordi; Cuesta, Jordi: Improving an interior-point algorithm for multicommodity flows by quadratic regularizations (2012)
  9. Castro, Jordi; Cuesta, Jordi: Quadratic regularizations in an interior-point method for primal block-angular problems (2011)
  10. Hall, J. A. J.: Towards a practical parallelisation of the simplex method (2010)
  11. Gonçalves, João P. M.; Storer, Robert H.; Gondzio, Jacek: A family of linear programming algorithms based on an algorithm by von Neumann (2009)
  12. Al-Jeiroudi, Ghussoun; Gondzio, Jacek; Hall, Julian: Preconditioning indefinite systems in interior point methods for large scale linear optimisation (2008)
  13. Herrero, José R.; Navarro, Juan J.: Hypermatrix oriented supernode amalgamation (2008)
  14. Castro, Jordi: An interior-point approach for primal block-angular problems (2007)
  15. Herrero, José R.; Navarro, Juan J.: Analysis of a sparse hypermatrix Cholesky with fixed-sized blocking (2007)
  16. Herrero, José R.; Navarro, Juan J.: Sparse hypermatrix Cholesky: customization for high performance (2007)
  17. Hall, J. A. J.; McKinnon, K. I. M.: Hyper-sparsity in the revised simplex method and how to exploit it (2005)
  18. Oliveira, A. R. L.; Sorensen, D. C.: A new class of preconditioners for large-scale linear systems from interior point methods for linear programming (2005)
  19. Pan, Ping-Qi: A revised dual projective pivot algorithm for linear programming (2005)
  20. Davis, Timothy A.; Gilbert, John R.; Larimore, Stefan I.; Ng, Esmond G.: Algorithm 836: COLAMD, a column approximate minimum degree ordering algorithm (2004)

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