We develop algorithms that find and track the optimal solution trajectory of time-varying convex optimization problems that consist of local and network-related objectives. The algorithms are derived from the prediction-correction methodology, which corresponds to a strategy where the time-varying problem is sampled at discrete time instances, and then, a sequence is generated via alternatively executing predictions on how the optimizers at the next time sample are changing and corrections on how they actually have changed. Prediction is based on how the optimality conditions evolve in time, while correction is based on a gradient or Newton method, leading to decentralized prediction-correction gradient and decentralized prediction-correction Newton. We extend these methods to cases where the knowledge on how the optimization programs are changing in time is only approximate and propose decentralized approximate prediction-correction gradient and decentralized approximate prediction-correction Newton. Convergence properties of all the proposed methods are studied and empirical performance is shown on an application of a resource allocation problem in a wireless network. We observe that the proposed methods outperform existing running algorithms by orders of magnitude. The numerical results showcase a tradeoff between convergence accuracy, sampling period, and network communications.@en

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the sampling period. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O({h}^{2})$, and in some cases as $O({h}^{4})$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

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We introduce DeNT, a decentralized Newton-based tracking algorithm that solves and track the solution trajectory of continuously varying networked convex optimization problems. DeNT is derived from the prediction-correction methodology, by which the time-varying optimization problem is sampled at discrete time instances and then a sequence is generated via alternatively executing predictions on how the optimizers are changing and corrections on how they actually have changed. Prediction is based on the sample dynamics of the optimality conditions, while correction is based on a Newton method. After presenting DeNT, we show how it can be implemented in a decentralized way and analyze its convergence. We extend it to cases in which the knowledge on how the optimization programs are changing in time is only approximate, proposing DeANT. We then present an application to a resource allocation problem in a wireless network, demonstrating the proposed method outperforms existing methods by orders of magnitude, and exhibits a trade-off between convergence accuracy, sampling interval, and network communications.@en