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.
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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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Andersen, E., Roos, C. & Terlaky, T. On implementing a primal-dual interior-point method for conic quadratic optimization. Math. Program., Ser. B 95, 249–277 (2003). https://doi.org/10.1007/s10107-002-0349-3
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DOI: https://doi.org/10.1007/s10107-002-0349-3