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

Author

Listed:
  • M. D. Sánchez

    (University of La Plata)

  • M. L. Schuverdt

    (University of La Plata)

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.

Suggested Citation

  • M. D. Sánchez & M. L. Schuverdt, 2019. "A second-order convergence augmented Lagrangian method using non-quadratic penalty functions," OPSEARCH, Springer;Operational Research Society of India, vol. 56(2), pages 390-408, June.
    • Handle: RePEc:spr:opsear:v:56:y:2019:i:2:d:10.1007_s12597-019-00366-3
    DOI: 10.1007/s12597-019-00366-3
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    References listed on IDEAS

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    1. Gabriel Haeser & María Laura Schuverdt, 2011. "On Approximate KKT Condition and its Extension to Continuous Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 149(3), pages 528-539, June.
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    Cited by:

    1. Kamyar Tolouei & Ehsan Moosavi & Mehran Gholinejad, 2024. "Solving LTPSOP in open-pit mines using Gaussian process and human mental search," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1061-1092, September.

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