期刊
MATHEMATICAL PROGRAMMING
卷 95, 期 2, 页码 249-277出版社
SPRINGER
DOI: 10.1007/s10107-002-0349-3
关键词
conic optimization; interior-point methods; large-scale implementation
Based on the work of the Nesterov and Todd on self-scaled cones an implementation of a primaldual 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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