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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...
We study networked unconstrained convex optimization problems where the objective function changes continuously in time. We propose a decentralized algorithm (DePCoT) with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and gradient-based correction steps, while sampling the problem data at a constant sampling period h. Under suitable conditions and for...
We develop a framework for trajectory tracking in dynamic settings, where an autonomous system is charged with the task of remaining close to an object of interest whose position varies continuously in time. We model this scenario as a convex optimization problem with a time-varying objective function and propose an adaptive discrete-time sampling prediction-correction scheme to find and track the...
Constrained optimization problems that couple different cooperating users sharing the same communication network are often referred to as multiuser optimization programs. We are interested in convex discrete-time time-varying multiuser optimization, where the problem to be solved changes at each time step. We study a distributed algorithm to generate a sequence of approximate optimizers of these problems...
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