Y13 Big O notation

Indicate constant time complexity in terms of Big-O notation

• O(n)

• O(1)

• O(log n)

• O (n^2)

Indicate exponential time complexity in terms of big-O notation

• O (n)

• O (n^2)

• O (2^n)

• O (log n)

Find the slowest time complexity

• O (n)

• O (n^2)

• O (n!)

• O (2^n)

Which notation is consistent for every execution?

• O (n)

• O (n^2)

• O (1)

• O (2^n)

The O (n!) is so inefficient, there is no practical use for it

• True

• False

Which notation grows in proportion to the size of the input

• O (n)

• O (n^2)

• O (1)

• O (2^n)

Which notation would you usually use for a nested loop?

• O (n)

• O (n^2)

• O (1)

• O (2^n)

Indicate polynomial time complexity in terms of big-O notation

• O (n!)

• O (1)

• O (n^2)

• O (log n)

When calculating the Big-O for an algorithm, which of the following rules is not true?

• Focus on the dominant term

• Disregard any constants

• Focus only on polynomials

• Count the number of assignments

A linear function takes the form

• f(n) = an + b

• f(n) = an2 +bn + c

• f(n) = alog2n

• f(n) = a + b