6533b825fe1ef96bd1281f8a

RESEARCH PRODUCT

Fast Decentralized Linear Functions via Successive Graph Shift Operators

Daniel RomeroSiavash MollaebrahimBaltasar Beferull-lozano

subject

Linear mapSignal Processing (eess.SP)Optimization problemTransformation (function)Theoretical computer scienceComputer scienceFOS: Electrical engineering electronic engineering information engineeringGraph (abstract data type)Shift matrixElectrical Engineering and Systems Science - Signal ProcessingShift operatorSubspace topologyEigenvalues and eigenvectors

description

Decentralized signal processing performs learning tasks on data distributed over a multi-node network which can be represented by a graph. Implementing linear transformations emerges as a key task in a number of applications of decentralized signal processing. Recently, some decentralized methods have been proposed to accomplish that task by leveraging the notion of graph shift operator, which captures the local structure of the graph. However, existing approaches have some drawbacks such as considering special instances of linear transformations, or reducing the family of transformations by assuming that a shift matrix is given such that a subset of its eigenvectors spans the subspace of interest. In contrast, this paper develops a decentralized method to compute linear transformations in a small number of iterations. To this end, a set of successive graph shift operators is designed. Hence, a new optimization problem is proposed whose goal is to compute the desired transformation as fast as possible.

yearjournalcountryeditionlanguage
2019-11-22
https://dx.doi.org/10.48550/arxiv.1911.10070