Understanding Algorithms

What is an algorithm?

• A type of computer hardware.

• A random collection of numbers.

• An algorithm is a step-by-step procedure for solving a problem.

• A recipe for cooking a meal.

What are the main characteristics of a good algorithm?

• Obscurity, inefficiency, correctness, and scalability.

• Speed, randomness, unpredictability, and fragility.

• Clarity, efficiency, correctness, robustness, scalability, and maintainability.

• Simplicity, complexity, ambiguity, and inflexibility.

Explain the difference between a linear search and a binary search.

• Linear search requires a sorted list to function.

• Linear search is faster than binary search.

• Linear search is O(n) and checks each element; binary search is O(log n) and divides the list.

• Binary search can be used on unsorted lists.

What is the time complexity of bubble sort?

• O(n^2)

• O(n)

• O(log n)

• O(n log n)

Define recursion in the context of algorithms.

• Recursion is a method that uses loops to repeat a process until a condition is met.

• Recursion is a way to optimize algorithms by reducing their time complexity.

• Recursion is a technique where a function iterates over a list of items.

• Recursion is a method in algorithms where a function solves a problem by calling itself with modified arguments.

What is a greedy algorithm? Give an example.

• An example of a greedy algorithm is the Coin Change Problem, where the goal is to make change for a given amount using the fewest coins possible. The algorithm selects the largest denomination coin that does not exceed the remaining amount until the total is reached.

• A greedy algorithm always finds the optimal solution for all problems.

• A greedy algorithm is one that uses dynamic programming to solve problems.

• An example of a greedy algorithm is sorting a list in ascending order.

Explain the concept of dynamic programming.

• Dynamic programming is a technique for solving problems without any prior knowledge of subproblems.

• Dynamic programming involves solving problems in a linear fashion without recursion.

• Dynamic programming is a method for solving problems by breaking them into overlapping subproblems and storing their solutions.

• Dynamic programming is a method that only applies to optimization problems without overlapping subproblems.

What is the purpose of a hash table in algorithms?

• To encrypt sensitive information securely.

• The purpose of a hash table is to enable fast data access and manipulation through key-value pairs.

• To store data in a linear format for easy retrieval.

• To create a backup of data for recovery purposes.

Describe the divide and conquer strategy.

• A method that only combines results without solving individual parts.

• A strategy that focuses on solving the entire problem at once without breaking it down.

• A technique that ignores smaller problems and addresses only the largest one.

• Divide and conquer is a problem-solving strategy that divides a problem into smaller parts, solves each part, and combines the results.

What is the significance of Big O notation in algorithm analysis?

• Big O notation is significant because it allows for the analysis and comparison of the efficiency of algorithms in terms of their time and space complexity.

• Big O notation is used to measure the popularity of algorithms.

• Big O notation helps in determining the exact runtime of an algorithm.

• Big O notation is primarily concerned with the programming language used.