We consider the sequential Bayesian op-timization problem with bandit feedback,adopting a formulation that allows for the re-ward function to vary with time. We modelthe reward function using a Gaussian pro-cess whose evolution obeys a simple Markovmodel. We introduce two natural extensionsof the classical Gaussian process upper confi-dence bound (GP-UCB) algorit...

This article considers a time-varying optimization problem associated with network of systems, each the systems shared by (and affecting) number individuals. The objective is to minimize cost functions individuals' preferences, which are unknown, subject constraints that capture physical or operational limits network. To this end, develops distributed online algorithm concurrent learning functi...

Solving optimization problems in multiagent systems involves information exchange between agents. The obtained solutions should be robust to delays and errors that arise from an unreliable wireless network, which typically connects the system. In today’s large-scale dynamic Internet of Things style scenarios, network topology changes evolves over time. this article, we present a simple distribu...

This paper considers a convex optimization problem with cost and constraints that evolve over time. The function to be minimized is strongly possibly non-differentiable, variables are coupled through linear constraints. In this setting, the proposes an online algorithm based on alternating direction method of multipliers (ADMM), track optimal solution trajectory time-varying problem; in particu...

An available bit rate (ABR) service allows applications to access a time-varying network capacity. In a basic ABR service, the available network capacity is divided \fairly" amongst active connections, without regard to the utility that each application derives from the bandwidth allocation. The goal of this paper is to improve both the global and individual utility obtained by applications usi...

In this paper we address a time-varying quadratic optimization problem over graph under the assumption that shares same sparsity pattern as static encoding undirected network topology which multi-agent systems interacts. Notably, framework allows to effectively model scenarios in is inherently embedded within topology, e.g., flow balancing, electrical power system managements or packet routing ...

Many exist studies always use Markov decision processes (MDPs) in modeling optimal route choice in stochastic, time-varying networks. However, taking many variable traffic data and transforming them into optimal route decision is a computational challenge by employing MDPs in real transportation networks. In this paper we model finite horizon MDPs using directed hypergraphs. It is shown that th...