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Sublinear-time-Survey-BEATCS.pdf
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Summary

Sublinear-time algorithms are a rapidly emerging area of computer science, driven by the need to manage and analyze massive data sets. Traditional efficient algorithms are often too slow for such large data sets, so there is a desire to develop algorithms with sublinear running times. Recent advances have led to the development of sublinear-time algorithms for optimization problems in graph theory, geometry, algebraic computations, and computer graphics. This survey focuses on sublinear-time algorithms for combinatorial problems, particularly graph problems and optimization problems in metric spaces. Examples of sublinear-time algorithms include searching in a sorted list, approximating the average degree in a graph, and estimating the cost of a minimum spanning tree. These algorithms are often randomized and return approximate solutions, as exact solutions typically require examining the entire input.

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Sublinear-time algorithms are a rapidly emerging area of computer science, driven by the need to manage and analyze massive data sets.

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  • File Format: PDF
  • File Size: 234 KB
  • Pages: 22
  • Language: EN
  • Total Downloads: 334
  • Last Updated: 1 month ago

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