Encyclopedia of Algorithms

2016 Edition
| Editors: Ming-Yang Kao

Local Reconstruction

  • Comandur SeshadhriEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-2864-4_698

Years and Authors of Summarized Original Work

  • 2010; Saks, Seshadhri

Problem Definition

Consider some massive dataset represented as a function f : DR, where D is discrete and R is an arbitrary range. This dataset could be as varied as an array of numbers, a graph, a matrix, or a high-dimensional function. Datasets are often useful because they possess some property of interest. An array might be sorted, a graph might be connected, a matrix might be orthogonal, or a function might be convex. These properties are critical to the use of the dataset. Yet, due to unavoidable errors (say, in storing the dataset), these properties might not hold any longer. For example, a sorted array could become unsorted because of roundoff errors.

Can we find a function g : DR that satisfies the property and is “sufficiently close” to f? Let us formalize this question. Let \(\mathcal{P}\)


Data reconstruction Property testing Sublinear algorithms 
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Recommended Reading

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Sandia National LaboratoriesLivermoreUSA
  2. 2.Department of Computer Science, University of CaliforniaSanta CruzUSA