Abstract
We propose a feasible primal-dual path-following interior-point algorithm for semidefinite least squares problems (SDLS). At each iteration, the algorithm uses only full Nesterov-Todd steps with the advantage that no line search is required. Under new appropriate choices of the parameter β which defines the size of the neighborhood of the central-path and of the parameter θ which determines the rate of decrease of the barrier parameter, we show that the proposed algorithm is well defined and converges to the optimal solution of SDLS. Moreover, we obtain the currently best known iteration bound for the algorithm with a short-update method, namely, \({\cal O}(\sqrt n \log (n/\varepsilon))\). Finally, we report some numerical results to illustrate the efficiency of our proposed algorithm.
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The research has been supported for the second author by: La Direction Générale de la Recherche Scientifique et du Développement Technologique (DGRSDT-MESRS), under project PRFU number C00L03UN190120190004, Algérie.
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Daili, C., Achache, M. An interior-point algorithm for semidefinite least-squares problems. Appl Math 67, 371–391 (2022). https://doi.org/10.21136/AM.2021.0217-20
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DOI: https://doi.org/10.21136/AM.2021.0217-20