On some interior-point algorithms for nonconvex quadratic optimization

被引：5
|
作者：
Tseng, P [1 ]
Ye, YY
机构：
[1] Univ Washington, Dept Math, Seattle, WA 98195 USA
[2] Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA
来源：
MATHEMATICAL PROGRAMMING | 2002年 / 93卷 / 02期
关键词：
nonconvex quadratic optimization; local minimum; interior-point algorithms; trust region; branch-and-cut;
D O I：
10.1007/s10107-002-0310-5
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：217 / 225
页数：9
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