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Themistoklis Charalambous
Researcher at Aalto University
Publications - 219
Citations - 3468
Themistoklis Charalambous is an academic researcher from Aalto University. The author has contributed to research in topics: Computer science & Relay. The author has an hindex of 27, co-authored 189 publications receiving 2770 citations. Previous affiliations of Themistoklis Charalambous include University of Cyprus & University of Waterloo.
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Proceedings ArticleDOI
Infinite horizon average cost dynamic programming subject to ambiguity on conditional distribution
TL;DR: This paper addresses the optimality of stochastic control strategies based on the infinite horizon average cost criterion, subject to total variation distance ambiguity on the conditional distribution of the controlled process, and derives a new dynamic programming recursion which minimizes the future ambiguity.
Proceedings ArticleDOI
A Fast Finite-Time Consensus based Gradient Method for Distributed Optimization over Digraphs
TL;DR: In this paper , the authors proposed a distributed FTERC-based algorithm for unconstrained optimization problem over directed strongly connected communication graphs, which combines techniques of both gradient descent and finite-time exact ratio consensus (FTERC).
Journal ArticleDOI
Traffic flow optimization with QoS constrained network admission control
Alfréd András Csikós,Hamed Farhadi,Hamed Farhadi,Balázs Kulcsár,Themistoklis Charalambous,Themistoklis Charalambous,Henk Wymeersch +6 more
TL;DR: A control design method is proposed in order to gate input flow to a protected urban vehicular network such that travel time Quality of Service (QoS) constraints are preserved within the network.
Proceedings ArticleDOI
Generalizing Nesterov’s Acceleration Framework by Embedding Momentum Into Estimating Sequences: New Algorithm and Bounds
TL;DR: A newly introduced momentum term is devised, as an example, a new black-box accelerated first-order method for solving smooth unconstrained optimization problems and it is proved that the proposed method exhibits an improvement over the rate of the celebrated fast gradient method by at least a factor of 1.
Posted Content
Lossless Coding with Generalised Criteria
TL;DR: This framework encompasses as a special case several criteria previously investigated in the literature, while relations to universal coding is discussed, including a convex combination of the average of an exponential function of the codeword length and the average redundancy.