This talk will present some different distributed, asynchronous optimization methods implemented in Skywing, a software platform for robust, decentralized algorithms produced at Lawrence Livermore National Lab. We focus on solving a column-partitioned, linear least squares problem, discussing problem formulation in the decentralized setting and the application of some different algorithms. Finally, we compare convergence rates and robustness to asynchrony among the different algorithm choices.