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.

引用

页码：217 / 225

页数：8

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