Theory of semidefinite programming for Sensor Network Localization

By Anthony Man-Cho So, Yinyu Ye. ACM-SIAM Symposium on Discrete Algorithms, 2005

467 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/235a54f586a5c6ded9251368f7c43d215ecfa258

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In Computational Mechanics

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Citations in the corpus

An Overview of Low-Rank Matrix Recovery From Incomplete Observations (2016, background): “To name just a few examples, we commonly encounter low-rank matrices in contexts as varied as: • ensembles of signals: the output of a sensor array or network, a collection of video frames, or a sequence of segments of a longer signal can often be highly correlated and represented using a low-rank m”
Visualizing data as objects by DC (difference of convex) optimization (2017, background): “Among them is the Sensor Network Location problem, [46, 48, 54, 58], in which one assumes that some individuals are anchors (their location is known) and the remaining ones are sensors, whose location is to be obtained so that their Eculidean distances fit the dissimilarities.”
Exact Matrix Completion via Convex Optimization (2008, background): “estimate the true distance matrix whose rank will be equal to two if the sensors are located in a plane or three if they are located in three dimensional space [25, 33 ].”
Convex Optimization & Euclidean Distance Geometry (2004, methodology): “The legacy of Carter & Jin provided by [47] is a sobering demonstration of the need for more efficient methods for solution of semidefinite programs, while that of So & Ye in [218] is the bonding of distance geometry to semidefinite programming.”
Approximation accuracy, gradient methods, and error bound for structured convex optimization (2010, background): “How well does (10) approximate (9)? A fourth problem of much recent interest is that of ad hoc wireless sensor network localization [4, 17, 18, 20, 21, 29, 36, 37, 47, 70, 72, 127].”