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