Algorithms and algorithmic Complexity

Which of the following is NOT a characteristic of an algorithm?

• A) Input

• B) Output

• C) Finiteness

• D) Memory Usage

• E) Feasibility

An algorithm must always end after executing a finite number of steps, and each step must be completed in a finite time. This characteristic is known as:

• A) Determinism

• B) Feasibility

• C) Finiteness

• D) Correctness

• E) Readability

Which of the following is a requirement when designing an algorithm?

• A) It must be computationally expensive

• B) It must be easy to understand, code, and debug

• C) It must always produce the fastest result

• D) It must use the minimum amount of storage

• E) It must require no input data

Which of the following is the primary disadvantage of ex post facto analysis of an algorithm’s efficiency?

• A) It ignores environmental factors like hardware and software

• B) It only estimates performance, not actual measurements

• C) It requires writing a program to implement the algorithm

• D) It cannot measure time complexity

• E) It doesn’t account for the algorithm's correctness

The time complexity of an algorithm is generally expressed as a function of the problem size n. This function is usually referred to as:

• A) Space Complexity

• B) Fundamental Operation

• C) Big-O Notation

Which of the following time complexities grows the fastest as n increases?

• O(1)

• O(n)

• O(n^2)

• O(n!)

• O(nlogn)

If an algorithm has a time complexity of O(n^2) and another has O(n^3), what is the time complexity of the sum of the two algorithms?

• O(n^2)

• O(n^3)

• O(n^4)

• O(n)

• O(nlogn)

Given the following code, what is its time complexity in Big-O notation?

• O(n)

• O(n^2)

• O(n^3)

• O(2n^2)

• O(n^2 + n)

In algorithmic performance analysis, the 'worst case' scenario refers to:

• The average behavior of an algorithm

• The case where the input data is most favorable for the algorithm

• The scenario that is least likely to happen

• The scenario where the algorithm performs the most work

In algorithmic complexity, the term "Big-O notation" is used to describe the _____ of an algorithm in terms of its time or space consumption.