Mathematical Programming

, Volume 95, Issue 2, pp 249–277

On implementing a primal-dual interior-point method for conic quadratic optimization

  • E.D. Andersen
  • C. Roos
  • T. Terlaky

DOI: 10.1007/s10107-002-0349-3

Cite this article as:
Andersen, E., Roos, C. & Terlaky, T. Math. Program., Ser. B (2003) 95: 249. doi:10.1007/s10107-002-0349-3

Abstract.

 Based on the work of the Nesterov and Todd on self-scaled cones an implementation of a primal-dual interior-point method for solving large-scale sparse conic quadratic optimization problems is presented. The main features of the implementation are it is based on a homogeneous and self-dual model, it handles rotated quadratic cones directly, it employs a Mehrotra type predictor-corrector extension and sparse linear algebra to improve the computational efficiency. Finally, the implementation exploits fixed variables which naturally occurs in many conic quadratic optimization problems. This is a novel feature for our implementation. Computational results are also presented to document that the implementation can solve very large problems robustly and efficiently.

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • E.D. Andersen
    • 1
  • C. Roos
    • 2
  • T. Terlaky
    • 3
  1. 1.MOSEK APS, Fruebjergvej 3 Box 16, 2100 Copenhagen O, Denmark, e-mail: e.d. andersen@mosek.comDK
  2. 2.TU Delft, Mekelweg 4, 2628 CD Delft, The Netherlands, e-mail: c.roos@its.tudelft.nlNL
  3. 3.McMaster University, Department of Computing and Software, Hamilton, Ontario, Canada, L8S 4L7. e-mail: terlaky@mcmaster.caCA

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