Primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric algebraic transformation
<|reference_start|>Primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric algebraic transformation: In this paper, we present an interior point algorithm with a full-Newton step for solving a linearly constrained convex optimization problem, in which we propose a generalization of the work of Kheirfam and Nasrollahi \cite{kheirfam2018full}, that consists in determining the descent directions through a parametric algebraic transformation. The work concludes with a complete study of the convergence of the algorithm and its complexity, where we show that the obtained algorithm achieves a polynomial complexity bounds.<|reference_end|>
@article{kraria2024primal-dual,
    title={Primal-dual interior-point algorithm for linearly constrained convex
  optimization based on a parametric algebraic transformation},
    author={Aicha Kraria and Bachir Merikhi and Djamel Benterki},
    journal={arXiv preprint arXiv:2403.11684},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11684},
    primaryClass={math.NA cs.NA math.OC}
}