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Convergence analysis of a mixed logarithmic barrier-augmented Lagrangian algorithm without constraint qualification

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  • Tran Ngoc Nguyen

    (Quy Nhon University)

Abstract

In this paper, we exploit some properties of points in a neighborhood of the solution set of degenerate optimization problems. Combining these facts with the boundedness of the inverse of regularized Jacobian matrix arising in a mixed logarithmic barrier-augmented lagrangian method, we propose an updating rule for parameters of a mixed logarithmic barrier-augmented Lagrangian algorithm. The superlinear convergence of this algorithm is then proved without any constraint qualification. Numerical results on degenerate problems are also presented to confirm theoretical results.

Suggested Citation

  • Tran Ngoc Nguyen, 2025. "Convergence analysis of a mixed logarithmic barrier-augmented Lagrangian algorithm without constraint qualification," Computational Optimization and Applications, Springer, vol. 91(3), pages 1105-1134, July.
    • Handle: RePEc:spr:coopap:v:91:y:2025:i:3:d:10.1007_s10589-025-00690-z
    DOI: 10.1007/s10589-025-00690-z
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