@article {IOPORT.05550707,
    author       = {Auslender, Alfred and Teboulle, Marc},
    title        = {Projected subgradient methods with non-Euclidean distances for non-differentiable convex minimization and variational inequalities.},
    year         = {2009},
    journal      = {Mathematical Programming. Series A. Series B},
    volume       = {120},
    number       = {1 (B)},
    issn         = {0025-5610},
    pages        = {27-48},
    publisher    = {Springer-Verlag, Berlin},
    doi          = {10.1007/s10107-007-0147-z},
    abstract     = {Summary: We study subgradient projection type methods for solving non-differentiable convex minimization problems and monotone variational inequalities. The methods can be viewed as a natural extension of subgradient projection type algorithms, and are based on using non-Euclidean projection-like maps, which generate interior trajectories. The resulting algorithms are easy to implement and rely on a single projection per iteration. We prove several convergence results and establish rate of convergence estimates under various and mild assumptions on the problem's data and the corresponding step-sizes.},
    identifier   = {05550707},
}