Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
This work addresses decentralized optimization problems for distributed systems, offering an incremental improvement with practical stepsize flexibility.
The paper tackles decentralized convex composite optimization with smooth and nonsmooth terms by proposing a CTA-based algorithm using network-independent stepsizes and inexact proximal mappings, achieving an O(1/k) convergence rate for general convex cases and linear convergence under metric subregularity.
This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for solving convex composite optimization problems over undirected and connected networks. The local loss function in these problems contains both smooth and nonsmooth terms. The proposed algorithm uses uncoordinated network-independent constant stepsizes and only needs to approximately solve a sequence of proximal mappings, which is advantageous for solving decentralized composite optimization problems where the proximal mappings of the nonsmooth loss functions may not have analytical solutions. For the general convex case, we prove an O(1/k) convergence rate of the proposed algorithm, which can be improved to o(1/k) if the proximal mappings are solved exactly. Furthermore, with metric subregularity, we establish a linear convergence rate for the proposed algorithm. Numerical experiments demonstrate the efficiency of the algorithm.