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, Volume 56, Issue 2, pp 390–408 | Cite as

A second-order convergence augmented Lagrangian method using non-quadratic penalty functions

  • M. D. Sánchez
  • M. L. SchuverdtEmail author
Theoretical Article
  • 45 Downloads

Abstract

The purpose of the present paper is to study the global convergence of a practical Augmented Lagrangian model algorithm that considers non-quadratic Penalty–Lagrangian functions. We analyze the convergence of the model algorithm to points that satisfy the Karush–Kuhn–Tucker conditions and also the weak second-order necessary optimality condition. The generation scheme of the Penalty–Lagrangian functions includes the exponential penalty function and the logarithmic-barrier without using convex information.

Keywords

Nonlinear programming Augmented Lagrangian methods Global convergence Constraint qualifications Sequential optimality conditions 

Notes

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Copyright information

© Operational Research Society of India 2019

Authors and Affiliations

  1. 1.CONICET, Department of Mathematics, FCEUniversity of La PlataLa PlataArgentina

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