can be generalized to the problem class . Then we apply it to following problems: average consensus, two compressed sensing problems (different from the Basis Pursuit), and support vector machines. We also provide extensive simulations of several distributed algorithms solving . Our simulations show that the proposed algorithm outperforms, in terms of the number of used communications, any of the previous algorithms. One of the above problems, the average consensus, has been extensively studied due to its importance and simplicity. For example, many distributed algorithms, which cannot be trivially generalized to solve , have been proposed to solve it. Our algorithm, designed to solve the generic problem , can also solve the average consensus problem and, as our simulations show, it outperforms a state-of-the-art consensus algorithm [@Olshevsky11-ConvergenceSpeedDistributedConsensusAveraging], for many networks of interest.

[[**Organization.**]{}]{} The paper is organized as follows. In section \[Sec:AlgDerivation\] we derive the algorithm, by manipulating the problem and then applying ADMM. Section \[Sec:RelatedAlgs\] is dedicated to related algorithms; in particular, we describe the competing algorithms and show how we compare them with ours. In section \[Sec:Applications\] we take problems from several areas and see how they can be recast as . Additionally, we analyze the resulting algorithm 