Module 1

When simply determining the order of magnitude of an algorithm using Big-Oh algorithm analysis, an instruction that evaluates to O(c) is equal to one that evaluates to O(1).

True

What is the common name for the growth rate function c?

constant

What is the common name for the growth rate function N?

linear

Which of the following growth-rate functions would correspond to the shortest running time?

linear

What is the common name for the growth rate function logN?

logarithmic

Which of the following growth-rate functions would correspond to the longest running time?

quadratic

Which of the following growth-rate functions would correspond to the shortest running time?

quadratic

Which type of analysis informs you of an algorithm's performance in the worst-case scenario?

Big-Oh (Ο)

Which type of analysis informs you of an algorithm's performance in the best-case scenario?

Big-Omega (Ω)

Which type of analysis informs you that the upper and lower bounds of an algorithm's complexity are equal?

Big-Theta (Θ)

When performing Big-Oh analysis of an algorithm, we generally:

Consider only very large values for N

Which of the following growth-rate functions would correspond to the shortest running time?

Constant

Which of the following shows a list of Big-Oh running times in order from slowest to fastest?

Correct O(N!), O(2ᴺ), O(N²), O(N), O(logN)

What is the common name for the growth rate function N³?

Cubic

In determining an algorithm's efficiency, space consumption should be ignored.

False

Which of the following growth-rate functions would correspond to the longest running time?

N log N

Which of the following programming constructs generally have the most influence on an algorithm's complexity?

Nested loops

Of the following choices, which algorithm is the fastest?

N²

What is the common name for the growth rate function 2ᴺ?

exponential

Which of the following growth-rate functions would correspond to the longest running time?

factorial

Big-Oh analysis tends to most accurately reflect an algorithm's running time when:

the size of the problem (i.e., the value of N) is large

When converting a polynomial expression to a Big-Oh expression, we always focus on:

the term with the largest exponent