QANM combines Nesterov-accelerated gradient descent with distributed finite-time quantized consensus to achieve linear convergence to a neighborhood of the optimum in unconstrained distributed optimization over directed graphs under strong convexity and smoothness.
Survey of distributed algorithms for resource allocation over multi-agent systems,
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A fully distributed primal-dual algorithm solves nonsmooth strongly convex problems with coupled constraints on time-varying digraphs at O(1/k) rate without communicating primal variables.
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Nesterov Accelerated Distributed Optimization with Efficient Quantized Communication
QANM combines Nesterov-accelerated gradient descent with distributed finite-time quantized consensus to achieve linear convergence to a neighborhood of the optimum in unconstrained distributed optimization over directed graphs under strong convexity and smoothness.
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Distributed Optimization with Coupled Constraints over Time-Varying Digraph
A fully distributed primal-dual algorithm solves nonsmooth strongly convex problems with coupled constraints on time-varying digraphs at O(1/k) rate without communicating primal variables.