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P. Berger, G. Hannak, G. Matz:
"Graph Signal Recovery via Primal-Dual Algorithms for Total Variation Minimization";
IEEE Journal of Selected Topics in Signal Processing, 11 (2017).

We consider the problem of recovering a smooth graph signal from noisy samples taken on a subset of graph nodes. The smoothness of the graph signal is quantified in terms of total variation. We formulate the signal recovery task as a convex optimization problem that minimizes the total variation of the graph signal while controlling its global or node-wise empirical error. We propose a first-order primal-dual algorithm to solve these total variation minimization problems. A distributed implementation of the algorithm is devised to handle large-dimensional applications efficiently. We use synthetic and real-world data to extensively compare the performance of our approach with state-of-the-art methods.

graph theory;signal reconstruction;Graph signal recovery;primal-dual algorithms;total variation minimization;node-wise empirical error;first-order primal-dual algorithm;Noise measurement;Signal processing algorithms;Minimization;Computational modeling;Opt

http://dx.doi.org/10.1109/JSTSP.2017.2726978

https://publik.tuwien.ac.at/files/publik_276642.pdf