Speakers
Description
In this talk, we consider distributed methods for solving optimization problems. In the distributed formulation, the target function is divided into parts, and each of these parts can be accessed only by a local agent/worker. We deal with the case where the local functions are ''similar'' to each other in some sense. Due to the ''similarity'' it is possible to achieve a significant acceleration of the theoretical guarantees of convergence of methods in terms of estimates on communication complexity. Besides the issue of convergence of algorithms and obtaining upper bounds, we touch upon lower complexity bounds and verify the optimality of the proposed methods. In the remaining time, we try to discuss the question of how we can ''break through'' the lower estimates and construct an even faster method, in particular, we additionally introduce the possibility of compressing the transmitted information, modify the proposed algorithms and obtain upper and lower bounds in a new formulation.
