A Globally Convergent Algorithm for Minimizing Over the Rotation Group of Quadratic Forms

Chaya Gurwitz, Michael L. Overton

Research output: Contribution to journalArticle

Abstract

We describe a numerical procedure for solving problems involving minimization over the rotation group of quadratic forms which arise in connection with problems of computer vision. The algorithm presented is a sequential quadratic programming method which takes advantage of the special structure of the problem constraints. We conclude by proving that our method is globally convergent.

Original languageEnglish (US)
Pages (from-to)1228-1232
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume11
Issue number11
DOIs
StatePublished - Nov 1989

Keywords

  • Nonlinear programming
  • projected Hessian
  • quadratic forms
  • rotation groups
  • sequential quadratic programming

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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