Search Results for author: Joao Xavier

Found 1 papers, 0 papers with code

Primal-dual methods for large-scale and distributed convex optimization and data analytics

no code implementations18 Dec 2019 Dusan Jakovetic, Dragana Bajovic, Joao Xavier, Jose M. F. Moura

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier"(often unconstrained) sub-problems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables.

Optimization and Control Information Theory Information Theory

Cannot find the paper you are looking for? You can Submit a new open access paper.