Abstract.
Recently, interior-point algorithms have been applied to nonlinear and nonconvex optimization. Most of these algorithms are either primal-dual path-following or affine-scaling in nature, and some of them are conjectured to converge to a local minimum. We give several examples to show that this may be untrue and we suggest some strategies for overcoming this difficulty.
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Received: June 26, 2000 / Accepted: April 2002 Published online: September 5, 2002
Key words. Nonconvex quadratic optimization – local minimum – interior-point algorithms – trust region – branch-and-cut
This research is supported by the National Science Foundation Grant CCR-9731273 and DMS-9703490.
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Tseng, P., Ye, Y. On some interior-point algorithms for nonconvex quadratic optimization. Math. Program., Ser. A 93, 217–225 (2002). https://doi.org/10.1007/s10107-002-0310-5
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DOI: https://doi.org/10.1007/s10107-002-0310-5