OOPS

Parallel interior point solver for structured linear programs. Issues of implementation of an object-oriented library for parallel interior-point methods are addressed. The solver can easily exploit any special structure of the underlying optimization problem. In particular, it allows a nested embedding of structures and by this means very complicated real-life optimization problems can be modelled. The efficiency of the solver is illustrated on several problems arising in the optimization of networks. The sequential implementation outperforms the state-of-the-art commercial optimization software. The parallel implementation achieves speed-ups of about 3.1-3.9 on 4-processors parallel systems and speed-ups of about 10-12 on 16-processors parallel systems.


References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Grass, Emilia; Fischer, Kathrin; Rams, Antonia: An accelerated L-shaped method for solving two-stage stochastic programs in disaster management (2020)
  2. Castro, Jordi; Nasini, Stefano: On geometrical properties of preconditioners in IPMs for classes of block-angular problems (2017)
  3. Castro, Jordi; Nasini, Stefano; Saldanha-da-Gama, Francisco: A cutting-plane approach for large-scale capacitated multi-period facility location using a specialized interior-point method (2017)
  4. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  5. Tappenden, Rachael; Richtárik, Peter; Gondzio, Jacek: Inexact coordinate descent: complexity and preconditioning (2016)
  6. Bocanegra, Silvana; Castro, Jordi; Oliveira, Aurelio R. L.: Improving an interior-point approach for large block-angular problems by hybrid preconditioners (2013)
  7. Castro, Jordi; Cuesta, Jordi: Solving ( L_1)-CTA in 3D tables by an interior-point method for primal block-angular problems (2013)
  8. Colombo, Marco; Grothey, Andreas: A decomposition-based crash-start for stochastic programming (2013)
  9. Lobachev, Oleg; Guthe, Michael; Loogen, Rita: Estimating parallel performance (2013) ioport
  10. Gondzio, Jacek: Interior point methods 25 years later (2012)
  11. Lubin, Miles; Petra, Cosmin G.; Anitescu, Mihai: The parallel solution of dense saddle-point linear systems arising in stochastic programming (2012)
  12. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  13. Castro, Jordi; Cuesta, Jordi: Quadratic regularizations in an interior-point method for primal block-angular problems (2011)
  14. Colombo, Marco; Gondzio, Jacek; Grothey, Andreas: A warm-start approach for large-scale stochastic linear programs (2011)
  15. Mészáros, Csaba: On the implementation of interior point methods for dual-core platforms (2010)
  16. Azevedo, Anibal Tavares; Oliveira, Aurelio Ribeiro Leite; Soares, Secundino: Interior point method for long-term generation scheduling of large-scale hydrothermal systems (2009)
  17. Colombo, Marco; Grothey, Andreas; Hogg, Jonathan; Woodsend, Kristian; Gondzio, Jacek: A structure-conveying modelling language for mathematical and stochastic programming (2009)
  18. Gondzio, Jacek; Grothey, Andreas: Exploiting structure in parallel implementation of interior point methods for optimization (2009)
  19. Necoara, I.; Suykens, J. A. K.: Interior-point Lagrangian decomposition method for separable convex optimization (2009)
  20. Davis, Timothy A.; Hager, William W.: Dual multilevel optimization (2008)

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