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Interpolation Once Binary Search over a Sorted List

Author

Listed:
  • Jun-Lin Lin

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
    Innovation Center for Big Data and Digital Convergence, Taoyuan 32003, Taiwan
    Zhen Ding Tech. Group—Yuan Ze University Joint R&D Center for Big Data, Taoyuan 32003, Taiwan)

Abstract

Searching over a sorted list is a classical problem in computer science. Binary Search takes at most log 2 ⁡ n + 1 tries to find an item in a sorted list of size n . Interpolation Search achieves an average time complexity of O ( log ⁡ log ⁡ n ) for uniformly distributed data. Hybrids of Binary Search and Interpolation Search are also available to handle data with unknown distributions. This paper analyzes the computation cost of these methods and shows that interpolation can significantly affect their performance—accordingly, a new method, Interpolation Once Binary Search (IOBS), is proposed. The experimental results show that IOBS outperforms the hybrids of Binary Search and Interpolation Search for nonuniformly distributed data.

Suggested Citation

  • Jun-Lin Lin, 2024. "Interpolation Once Binary Search over a Sorted List," Mathematics, MDPI, vol. 12(9), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1394-:d:1387956
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