HOPDM

HOPDM is a package for solving large scale linear, convex quadratic and convex nonlinear programming problems. The code is an implementation of the infeasible primal-dual interior point method. It uses multiple centrality correctors; their number is chosen appropriately for a given problem in order to reduce the overall solution time. HOPDM automatically chooses the most efficient factorization method for a given problem (either normal equations or augmented system). The code compares favourably with commercial LP, QP and NLP packages.


References in zbMATH (referenced in 73 articles , 1 standard article )

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  1. Tsionas, Mike G.: A coherent approach to Bayesian data envelopment analysis (2020)
  2. 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)
  3. Gondzio, J.; Sobral, F. N. C.: Quasi-Newton approaches to interior point methods for quadratic problems (2019)
  4. Petra, Cosmin G.; Potra, Florian A.: A homogeneous model for monotone mixed horizontal linear complementarity problems (2019)
  5. Gondzio, Jacek; González-Brevis, Pablo: A new warmstarting strategy for the primal-dual column generation method (2015)
  6. Gondzio, Jacek; González-Brevis, Pablo; Munari, Pedro: New developments in the primal-dual column generation technique (2013)
  7. Gonzalez-Lima, María D.; Oliveira, Aurelio R. L.; Oliveira, Danilo E.: A robust and efficient proposal for solving linear systems arising in interior-point methods for linear programming (2013)
  8. Munari, Pedro; Gondzio, Jacek: Using the primal-dual interior point algorithm within the branch-price-and-cut method (2013)
  9. Friedlander, M. P.; Orban, D.: A primal-dual regularized interior-point method for convex quadratic programs (2012)
  10. Khorramizadeh, Mostafa: On solving Newton systems of primal-dual infeasible interior point methods using ABS methods (2012)
  11. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  12. Stoyan, Yuriy; Yaskov, Georgiy: Packing congruent hyperspheres into a hypersphere (2012)
  13. Zverovich, Victor; Fábián, Csaba I.; Ellison, Eldon F. D.; Mitra, Gautam: A computational study of a solver system for processing two-stage stochastic LPs with enhanced Benders decomposition (2012)
  14. Bergamaschi, Luca; Gondzio, Jacek; Venturin, Manolo; Zilli, Giovanni: Erratum to: Inexact constraint preconditioners for linear systems arising in interior point methods (2011)
  15. Colombo, Marco; Gondzio, Jacek; Grothey, Andreas: A warm-start approach for large-scale stochastic linear programs (2011)
  16. Munari, Pedro; González-Brevis, Pablo; Gondzio, Jacek: A note on the primal-dual column generation method for combinatorial optimization (2011)
  17. Woodsend, Kristian; Gondzio, Jacek: Exploiting separability in large-scale linear support vector machine training (2011)
  18. D’Apuzzo, Marco; De Simone, Valentina; di Serafino, Daniela: On mutual impact of numerical linear algebra and large-scale optimization with focus on interior point methods (2010)
  19. Stoyan, Yu. G.; Yaskov, G. N.: Packing identical spheres into a cylinder (2010)
  20. Al-Jeiroudi, G.; Gondzio, J.: Convergence analysis of the inexact infeasible interior-point method for linear optimization (2009)

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