CSC 203 ALGORITHM & COMPLEXITY

1. What is an algorithm?

• 1. A type of programming language

• 1. A sequence of steps designed to solve a specific problem

• 1. A type of computer hardware

• A set of data used in a database

Tags

NGSS.3-5-ETS1-2

1. Which of the following is NOT a characteristic of a good algorithm?

• Randomness

• Efficiency

• 1. Clarity

• Finiteness

1. What is the primary purpose of analyzing an algorithm’s complexity?

• 1. To determine the accuracy of the algorithm

• 1. To measure the algorithm's performance in terms of time and space

• 1. To understand the syntax of the algorithm

• 1. To improve the readability of the algorithm

4. Why is it important to have a clear definition of an algorithm's input and output?

• 4. To reduce the number of steps in the algorithm

• 4. To make the algorithm easier to understand

• 4. To ensure the algorithm operates on the correct data and produces the desired results

• 4. To increase the speed of the algorithm

4. How does the efficiency of an algorithm impact its usability in real-world applications?

• 4. More efficient algorithms use fewer resources, making them suitable for larger datasets

• 4. Efficiency determines how easily an algorithm can be rewritten

• 4. Efficiency only affects the programming language used

• 4. Less efficient algorithms are preferred for smaller tasks

6. Given a simple algorithm that adds all elements in an array, what is the best way to determine its efficiency?

• 6. Count the number of arithmetic operations needed for an input size nnn

• Measure the physical size of the array

• 6. Analyze the storage space of the array

• 6. Evaluate the complexity of the algorithm using assembly code

6. If you have an algorithm that processes each element of an array exactly once, what is the likely time complexity?

• O(n)O(n)O(n)

• O(n2)O(n^2)O(n2)

• O(log⁡n)O(\log n)O(logn)

• O(1)O(1)O(1)

8. Consider two algorithms, A and B, for solving the same problem. Algorithm A has a time complexity of O(n2)O(n^2)O(n2), while Algorithm B has O(nlog⁡n)O(n \log n)O(nlogn). What can be inferred about their performance?

• 8. Algorithm A is always faster than Algorithm B

• 8. Algorithm B is generally more efficient for larger inputs

• 8. Both algorithms will perform similarly for small inputs

• Algorithm A is more efficient for all input sizes

8. When analyzing an algorithm, why is it important to consider both time and space complexity?

• 8. Because neither impacts the practical use of the algorithm

• 8. Because space complexity is always more important than time complexity

• 8. Because only time complexity affects the overall performance

• 8. Because both determine the efficiency and feasibility of the algorithm for different applications

10. If you have a choice between an algorithm that uses constant space and one that uses linear space, what should be considered when deciding which to use?

• 10. The programming language used to implement the algorithm

• 10. The hardware limitations and input size

• The style and readability of the code

• 10. The number of lines of code in the algorithm