Search Machine Learning Repository: @inproceedings{icml2014c2_schwing14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm},
    Url = {},
    Abstract = {While MAP inference is typically intractable for many real-world applications, linear programming relaxations have been proven very effective. Dual block-coordinate descent methods are among the most efficient solvers, however, they are prone to get stuck in sub-optimal points. Although subgradient approaches achieve global convergence, they are typically slower in practice. To improve convergence speed, algorithms which compute the steepest $\epsilon$-descent direction by solving a quadratic program have been proposed. In this paper we suggest to decouple the quadratic program based on the Frank-Wolfe approach. This allows us to obtain an efficient and easy to parallelize algorithm while retaining the global convergence properties. Our method proves superior when compared to existing algorithms on a set of spin-glass models and protein design tasks.},
    Author = {Alexander Schwing and Tamir Hazan and Marc Pollefeys and Raquel Urtasun},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {487-495}