## Abstract
In this talk we will focus on distributed optimization algorithms. We will discuss optimization methods based on inference in graphical models, like the mini-sum and related algorithms, and methods based on convex optimization, like the alternating direction of multipliers method (ADMM) and variations of it. With respect to inference-based algorithm, we will focus on the (generalized) linear-coordinate descent algorithm, an iterative optimization algorithm with a convergence rate comparable to that of the min-sum algorithm, but with significantly less parameters to transmit per iteration. With respect to convex optimization based algorithms, we will focus on ADMM, a simple but powerful algorithm that is well suited to distributed convex optimization, and the bi-alternating direction method of multipliers (BiADMM), an algorithm that iteratively minimizes an augmented bi-conjugate function. The convergence of BiADMM is naturally established. Unlike ADMM that always involves three updates per iteration, BiADMM only needs two coordinate-descent operations. |