Optimal Methods for Convex Risk Averse Distributed Optimization

Understand

This paper studies the communication complexity of convex risk-averse optimization over a network.

• The problem generalizes the well-studied risk-neutral finite-sum distributed optimization problem and its importance stems from the need to handle risk in an uncertain environment.
• For algorithms in the literature, there exists a gap in communication complexities for solving risk-averse and risk-neutral problems.
• We propose two distributed algorithms, namely the distributed risk averse optimization (DRAO) method and the distributed risk averse optimization with sliding (DRAO-S) method, to close the gap.