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Globally Convergent Interior-Point Algorithm for Nonlinear Programming

Author

Listed:
  • I. Akrotirianakis

    (Princeton University)

  • B. Rustem

    (Imperial College)

Abstract

This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way.

Suggested Citation

  • I. Akrotirianakis & B. Rustem, 2005. "Globally Convergent Interior-Point Algorithm for Nonlinear Programming," Journal of Optimization Theory and Applications, Springer, vol. 125(3), pages 497-521, June.
    • Handle: RePEc:spr:joptap:v:125:y:2005:i:3:d:10.1007_s10957-005-2086-2
    DOI: 10.1007/s10957-005-2086-2
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    Citations

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    Cited by:

    1. B. Rustem & S. Žaković & P. Parpas, 2008. "Convergence of an Interior Point Algorithm for Continuous Minimax," Journal of Optimization Theory and Applications, Springer, vol. 136(1), pages 87-103, January.
    2. E. Obasanjo & G. Tzallas-Regas & B. Rustem, 2010. "An Interior-Point Algorithm for Nonlinear Minimax Problems," Journal of Optimization Theory and Applications, Springer, vol. 144(2), pages 291-318, February.

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