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Big O Notation and Algorithm Efficiency

Time complexity of linear and binary search

  • 3 things to know
  • Component 1

Overview

Worst-case time complexity of a linear search, why a linear search is O(n) and best case of a linear search.

What you need to know

Answer each one before you reveal it.

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Key term

State the worst-case time complexity of a linear search.

Key term

Explain why a linear search is O(n).

Key term

State the best case of a linear search.

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    State the worst-case time complexity of a linear search.

    Model answer

    A linear search has a worst case of O(n).

  2. 2

    Explain why a linear search is O(n).

    Model answer

    A linear search examines items one at a time from the start. The target may be the last item or absent, so up to n comparisons are made.

  3. 3

    State the best case of a linear search.

    Model answer

    The best case is O(1), reached when the target is the very first item examined.

  4. 4

    State the average number of comparisons a successful linear search makes on a list of n items.

    Model answer

    A successful linear search averages about n/2 comparisons, which is still O(n).

  5. 5

    Express the efficiency of a binary search in Big O notation.

    Model answer

    A binary search is O(log n).

  6. 6

    Explain why a binary search is O(log n).

    Model answer

    Each comparison discards half of the items still under consideration. The number of halvings needed to reduce n items to one is log₂ n.

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