Mathematical Programming

, Volume 93, Issue 2, pp 217–225 | Cite as

On some interior-point algorithms for nonconvex quadratic optimization

  • Paul Tseng
  • Yinyu Ye

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.

Keywords

Local Minimum Quadratic Optimization Nonconvex Optimization Nonconvex Quadratic Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Paul Tseng
    • 1
  • Yinyu Ye
    • 2
  1. 1.Department of Mathematics, University of Washington, Seattle, Washington 98195, USA, e-mail: tseng@math.washington.eduUS
  2. 2.Department of Management Science, University of Iowa, Iowa City, Iowa 52242, USA, e-mail: yinyu-ye@uiowa.eduUS

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