Big O Notation Flashcards

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1
Q

linear search best case time complexity

A

O(1)

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2
Q

linear search average case time complexity

A

O(n)

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3
Q

linear search worst case time complexity

A

O(n)

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4
Q

Binary search array best case time complexity

A

O(1)

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5
Q

Binary search array average case time complexity

A

O(log n)

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6
Q

Binary search array worst case time complexity

A

O(log n)

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7
Q

Binary search tree best case time complexity

A

O(1)

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8
Q

Binary search tree average case time complexity

A

O(log n)

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9
Q

Binary search tree worst case time complexity

A

O(n)

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10
Q

Hashing best case time complexity

A

O(1)

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11
Q

Hashing average case time complexity

A

O(1)

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12
Q

Hashing worst case time complexity

A

O(n)

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13
Q

Breadth/depth-first of graph best case time complexity

A

O(1)

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14
Q

Breadth/depth-first of graph average case time complexity

A

O(V+E)

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15
Q

Breadth/depth-first of graph worst case time complexity

A

O(V^2)

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16
Q

Bubble sort best case time complexity

A

O(n)

17
Q

Bubble sort average case time complexity

A

O(n^2)

18
Q

Bubble sort worst case time complexity

A

O(n^2)

19
Q

Bubble sort space complexity

A

O(1)

20
Q

Insertion sort best case time complexity

A

O(n)

21
Q

Insertion sort average case time complexity

A

O(n^2)

22
Q

Insertion sort worst case time complexity

A

O(n^2)

23
Q

Insertion sort space complexity

A

O(1)

24
Q

Merge sort best case time complexity

A

O(n log n)

25
Q

Merge sort average case time complexity

A

O(n log n)

26
Q

Merge sort worst case time complexity

A

O(n log n)

27
Q

Merge sort space complexity

A

O(n)

28
Q

Quick sort best case time complexity

A

O(n log n)

29
Q

Quick sort average case time complexity

A

O(n log n)

30
Q

Quick sort worst case time complexity

A

O(n^2)

31
Q

Quick sort space complexity

A

O(log n)

32
Q

Meaning of O(1)

A

Constant
same time regardless of size of data set

33
Q

Meaning of O(log n)

A

Logarithmic
halves the data set in each pass
opposite of exponential
directly proportional to logarithmic result of input size

34
Q

Meaning of O(n)

A

Linear
time increases proportional to size of data set

35
Q

Meaning of O(n log n)

A

Linearithmic

36
Q

Meaning of O(n^2)

A

Polynomial
time increases proportional to square of size of data set
nested loops

37
Q

Meaning of O(2^n)

A

Exponential
doubles with each addition to data set in each pass
opposite to logarithmic