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We study a deterministic primal-dual subgradient method for distributed optimization of a separable objective function with global inequality constraints. To control the norm of dual variables, we augment the Lagrangian function with a regularizer on dual variables. Specifically, we show that under a certain restriction on the step size of the underlying algorithm, the norm of dual variables is inversely...
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