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Overall Objectives
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Bibliography
Overall Objectives
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Bibliography


Section: New Results

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

In [35], we consider the distributed optimization of non-smooth convex functions using a network of computing units. We investigate this problem under two regularity assumptions: (1) the Lipschitz continuity of the global objective function, and (2) the Lipschitz continuity of local individual functions. Under the local regularity assumption, we provide the first optimal first-order decentralized algorithm called multi-step primal-dual (MSPD) and its corresponding optimal convergence rate. A notable aspect of this result is that, for non-smooth functions, while the dominant term of the error is in O(1/t), the structure of the communication network only impacts a second-order term in O(1/t), where t is time. In other words, the error due to limits in communication resources decreases at a fast rate even in the case of non-strongly-convex objective functions. Under the global regularity assumption, we provide a simple yet efficient algorithm called distributed randomized smoothing (DRS) based on a local smoothing of the objective function, and show that DRS is within a d1/4 multiplicative factor of the optimal convergence rate, where d is the underlying dimension.