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The Mosek Interior Point Optimizer for Linear Programming: An Implementation of the Homogeneous Algorithm

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High Performance Optimization

Part of the book series: Applied Optimization ((APOP,volume 33))

Abstract

The purpose of this work is to present the MOSEK optimizer intended for solution of large-scale sparse linear programs. The optimizer is based on the homogeneous interior-point algorithm which in contrast to the primal-dual algorithm detects a possible primal or dual infeasibility reliably. It employs advanced (parallelized) linear algebra, it handles dense columns in the constraint matrix efficiently, and it has a basis identification procedure.

This paper discusses in details the algorithm and linear algebra em­ployed by the MOSEK interior point optimizer. In particular the ho­mogeneous algorithm is emphasized. Furthermore, extensive computa­tional results are reported. These results include comparative results for the XPRESS simplex and the MOSEK interior point optimizer. Fi­nally, computational results are presented to demonstrate the possible speed-up, when using a parallelized version of the MOSEK interior point optimizer on a multiprocessor Silicon Graphics computer.

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Authors
  1. Erling D. Andersen
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  2. Knud D. Andersen
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Editor information

Editors and Affiliations

  1. Erasmus University, Rotterdam, The Netherlands

    Hans Frenk  & Shuzhong Zhang  & 

  2. Delft University of Technology, The Netherlands

    Kees Roos  & Tamás Terlaky  & 

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© 2000 Springer Science+Business Media Dordrecht

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Andersen, E.D., Andersen, K.D. (2000). The Mosek Interior Point Optimizer for Linear Programming: An Implementation of the Homogeneous Algorithm. In: Frenk, H., Roos, K., Terlaky, T., Zhang, S. (eds) High Performance Optimization. Applied Optimization, vol 33. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3216-0_8

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  • DOI: https://doi.org/10.1007/978-1-4757-3216-0_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4819-9

  • Online ISBN: 978-1-4757-3216-0

  • eBook Packages: Springer Book Archive

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