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Novel Interior Point Algorithms for Solving Nonlinear Convex Optimization Problems

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  • Sakineh Tahmasebzadeh
  • Hamidreza Navidi
  • Alaeddin Malek

Abstract

This paper proposes three numerical algorithms based on Karmarkar’s interior point technique for solving nonlinear convex programming problems subject to linear constraints. The first algorithm uses the Karmarkar idea and linearization of the objective function. The second and third algorithms are modification of the first algorithm using the Schrijver and Malek-Naseri approaches, respectively. These three novel schemes are tested against the algorithm of Kebiche-Keraghel-Yassine (KKY). It is shown that these three novel algorithms are more efficient and converge to the correct optimal solution, while the KKY algorithm fails in some cases. Numerical results are given to illustrate the performance of the proposed algorithms.

Suggested Citation

  • Sakineh Tahmasebzadeh & Hamidreza Navidi & Alaeddin Malek, 2015. "Novel Interior Point Algorithms for Solving Nonlinear Convex Optimization Problems," Advances in Operations Research, Hindawi, vol. 2015, pages 1-7, September.
    • Handle: RePEc:hin:jnlaor:487271
    DOI: 10.1155/2015/487271
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    Cited by:

    1. Bothina El-Sobky & Yousria Abo-Elnaga & Abd Allah A. Mousa & Mohamed A. El-Shorbagy, 2021. "Trust-Region Based Penalty Barrier Algorithm for Constrained Nonlinear Programming Problems: An Application of Design of Minimum Cost Canal Sections," Mathematics, MDPI, vol. 9(13), pages 1-20, July.

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