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A truncated SQP algorithm for solving nonconvex equality constrained optimization problems

  • Laurent Chauvier
  • Antonio Fuduli
  • Charles Jean Gilbert
Part of the Applied Optimization book series (APOP, volume 82)

Abstract

An algorithm for solving equality constrained optimization problems is proposed. It can deal with nonconvex functions and uses the truncated conjugate gradient algorithm for detecting nonconvexity. The algorithm ensures convergence from remote starting point by using line-search. Numerical experiments are reported, comparing the approach with the one implemented in the trust region codes ETR and Knitro.

Keywords

equality constraint exact penalty function global convergence line-search Newton’s method nonconvex optimization sequential quadratic programming truncated conjugate gradient algorithm 

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Copyright information

© Kluwer Academic Publishers B.V. 2003

Authors and Affiliations

  • Laurent Chauvier
    • 1
  • Antonio Fuduli
    • 2
  • Charles Jean Gilbert
    • 3
  1. 1.ArtelysIssy-les-Moulineaux CedexFrance
  2. 2.Dipartimento di Ingegneria dell’InnovazioneUniversità di LecceLecceItaly
  3. 3.INRIA RocquencourtLe Chesnay CedexFrance

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