OSL

Implementing interior point linear programming methods in the Optimization Subroutine Library. This paper discusses the implementation of interior point (barrier) methods for linear programming within the framework of the IBM Optimization Subroutine Library. This class of methods uses quite different computational kernels than the traditional simplex method. In particular, the matrices we must deal with are symmetric and, although still sparse, are considerably denser than those assumed in simplex implementations. Severe rank deficiency must also be accommodated, making it difficult to use off-the-shelf library routines. These features have particular implications for the exploitation of the newer IBM machine architectural features. In particular, interior methods can benefit greatly from use of vector architectures on the IBM $3090^{TM}$ series computers and “super-scalar” processing on the RISC System/$6000^{TM}$ series.


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  1. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  2. Sokolinskaya, I. M.; Sokolinskii, L. B.: Parallel algorithm for solving linear programming problem under conditions of incomplete data (2010)
  3. Liao, Chao-Ning; Önal, Hayri; Chen, Ming-Hsiang: Average shadow price and equilibrium price: a case study of tradable pollution permit markets (2009)
  4. Quintero, José Luis; Crema, Alejandro: An algorithm for multiparametric 0-1-integer programming problems relative to a generalized min Max objective function (2009)
  5. Xia, Yu: A global optimization method for semi-supervised clustering (2009) ioport
  6. Boschetti, M. A.; Mingozzi, A.; Ricciardelli, S.: A dual ascent procedure for the set partitioning problem (2008)
  7. Zeng, Liangzhao; Ngu, Anne H. H.; Benatallah, Boualem; Podorozhny, Rodion; Lei, Hui: Dynamic composition and optimization of web services (2008) ioport
  8. Dahal, Keshav P.; Aldridge, Chris J.; Galloway, Stuart J.: Evolutionary hybrid approaches for generation scheduling in power systems (2007)
  9. Long, Christopher E.; Polisetty, Pradeep K.; Gatzke, Edward P.: Deterministic global optimization for nonlinear model predictive control of hybrid dynamic systems (2007)
  10. Peters, Emmanuel; De Matta, Renato; Boe, Warren: Short-term work scheduling with job assignment flexibility for a multi-fleet transport system (2007)
  11. Sylva, John; Crema, Alejandro: A method for finding well-dispersed subsets of non-dominated vectors for multiple objective mixed integer linear programs (2007)
  12. Thompson, Gary M.; Pullman, Madeleine E.: Scheduling workforce relief breaks in advance versus in real-time (2007)
  13. Barahona, Francisco; Bermon, Stuart; Gnlk, Oktay; Hood, Sarah: Robust capacity planning in semiconductor manufacturing (2005)
  14. Fréville, Arnaud; Hanafi, Saïd: The multidimensional 0-1 knapsack problem -- bounds and computational aspects (2005)
  15. Fügenschuh, Armin; Martin, Alexander: Computational integer programming and cutting planes (2005)
  16. Ladanyi, Laszlo; Lee, Jon; Lougee-Heimer, Robin: Rapid prototyping of optimization algorithms using COIN-OR: a case study involving the cutting-stock problem (2005)
  17. Quintero, José Luis; Crema, Alejandro: An algorithm for multiparametric min max 0-1-integer programming problems relative to the objective function (2005)
  18. Tsodikov, Yu. M.; Tsodikova, Ya. Yu.: Regularizing functions in many-period production optimization models (2005)
  19. Fréville, Arnaud: The multidimensional 0-1 knapsack problem: an overview. (2004)
  20. Sylva, John; Crema, Alejandro: A method for finding the set of non-dominated vectors for multiple objective integer linear programs (2004)

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