@article{,
    author = {De Marchi, Alberto},
    title = {On a primal‑dual Newton proximal method for convex quadratic programs},
    editor = {},
    booktitle = {},
    series = {},
    journal = {Computational Optimization and Applications},
    address = {},
    publisher = {},
    edition = {},
    year = {2022},
    isbn = {},
    volume = {81},
    number =  {},
    pages = {369-395},
    url = {https://doi.org/10.1007/s10589-021-00342-y},
    doi = {10.1007/s10589-021-00342-y},
    keywords = {},
    abstract = {This paper introduces QPDO, a primal-dual method for convex quadratic programs which builds upon and weaves together the proximal point algorithm and a damped semismooth Newton method. The outer proximal regularization yields a numerically stable method, and we interpret the proximal operator as the unconstrained minimization of the primal-dual proximal augmented Lagrangian function. This allows the inner Newton scheme to exploit sparse symmetric linear solvers and multi-rank factorization updates. Moreover, the linear systems are always solvable independently from the problem data and exact linesearch can be performed. The proposed method can handle degenerate problems, provides a mechanism for infeasibility detection, and can exploit warm starting, while requiring only convexity. We present details of our open-source C implementation and report on numerical results against state-of-the-art solvers. QPDO proves to be a simple, robust, and efficient numerical method for convex quadratic programming.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Luft- und Raumfahrttechnik, LRT 1 - Institut für Angewandte Mathematik und Wissenschaftliches Rechnen, Professur: Gerdts, Matthias},
    
    
}