On implementing a primal-dual interior-point method for conic quadratic optimization |
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Authors: | ED Andersen C Roos T Terlaky |
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Institution: | (1) MOSEK APS, Fruebjergvej 3 Box 16, 2100 Copenhagen O, Denmark, e-mail: e.d. andersen@mosek.com, DK;(2) TU Delft, Mekelweg 4, 2628 CD Delft, The Netherlands, e-mail: c.roos@its.tudelft.nl, NL;(3) McMaster University, Department of Computing and Software, Hamilton, Ontario, Canada, L8S 4L7. e-mail: terlaky@mcmaster.ca, CA |
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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.
Received: November 18, 2000 / Accepted: January 18, 2001 Published online: September 27, 2002
Key Words. conic optimization – interior-point methods – large-scale implementation |
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