On some interior-point algorithms for nonconvex quadratic optimization

被引：0
|
作者：
Paul Tseng
Yinyu Ye
机构：
[1] Department of Mathematics,
[2] University of Washington,undefined
[3] Seattle,undefined
[4] Washington 98195,undefined
[5] USA,undefined
[6] e-mail: tseng@math.washington.edu,undefined
[7] Department of Management Science,undefined
[8] University of Iowa,undefined
[9] Iowa City,undefined
[10] Iowa 52242,undefined
[11] USA,undefined
[12] e-mail: yinyu-ye@uiowa.edu,undefined
来源：
Mathematical Programming | 2002年 / 93卷
关键词：
Local Minimum; Quadratic Optimization; Nonconvex Optimization; Nonconvex Quadratic Optimization;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：217 / 225
页数：8
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