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Constrained Optimization: Local Methods

  • Alexey F. Izmailov
  • Mikhail V. Solodov
Chapter
Part of the Springer Series in Operations Research and Financial Engineering book series (ORFE)

Abstract

This chapter is the central part of the book; it is devoted to local convergence analysis of Newton-type methods for optimization with constraints. The perturbed sequential quadratic programming (SQP) framework is introduced, which allows to treat, in addition and in a unified manner, not only various modifications of SQP itself (truncated, augmented Lagrangian based versions, and second-order corrections) but also a number of other algorithms even though their iterative subproblems are very different from SQP (linearly constrained Lagragian algorithm, sequential quadratically constrained quadratic programming, inexact restoration, and a certain interior feasible directions method).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexey F. Izmailov
    • 1
  • Mikhail V. Solodov
    • 2
  1. 1.Moscow State UniversityMoscowRussia
  2. 2.Instituto de Matemática Pura e AplicadaRio de JaneiroBrazil

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