What questions about algorithm analysis should you prepare for in an Algorithm developer interview?

1 Time and space complexity

One of the most fundamental aspects of algorithm analysis is measuring the time and space complexity of an algorithm. Time complexity refers to how fast an algorithm runs as a function of the input size, while space complexity refers to how much memory an algorithm uses as a function of the input size. You should be familiar with the concepts of asymptotic notation, such as Big O, Big Theta, and Big Omega, and how to use them to express the upper, lower, and tight bounds of an algorithm's complexity. You should also be able to compare the complexity of different algorithms and explain why one might be preferable over another. For example, you might be asked to compare the time and space complexity of different sorting algorithms, such as bubble sort, merge sort, and quick sort.

2 Recursion and iteration

Another common topic in algorithm analysis is recursion and iteration. Recursion is a technique where a function calls itself repeatedly until a base case is reached, while iteration is a technique where a loop repeats a set of instructions until a condition is met. Both recursion and iteration can be used to solve many problems, such as traversing a tree, searching an array, or calculating a factorial. You should be able to identify when recursion or iteration is appropriate for a given problem, and how to implement them correctly and efficiently. You should also be able to analyze the advantages and disadvantages of recursion and iteration, such as readability, simplicity, stack space, and time complexity. For example, you might be asked to write a recursive and an iterative version of a function that computes the nth Fibonacci number, and compare their performance.

3 Data structures and algorithms

Another important topic in algorithm analysis is data structures and algorithms. Data structures are ways of organizing and storing data, such as arrays, lists, stacks, queues, trees, graphs, and hash tables. Algorithms are steps or rules for solving problems, such as sorting, searching, hashing, encryption, compression, and graph algorithms. You should be able to choose the appropriate data structure and algorithm for a given problem, and explain how they work and why they are suitable. You should also be able to implement them using your preferred programming language, and test them for correctness and efficiency. For example, you might be asked to implement a hash table using an array and a linked list, and demonstrate how it handles collisions and resizing.

4 Optimization and trade-offs

Another relevant topic in algorithm analysis is optimization and trade-offs. Optimization is the process of finding the best possible solution for a problem, while trade-offs are the compromises or sacrifices that have to be made to achieve that solution. You should be able to identify the criteria and constraints for optimizing a problem, such as time, space, accuracy, reliability, scalability, security, and usability. You should also be able to evaluate the trade-offs between different solutions, and justify your choices and assumptions. For example, you might be asked to optimize a function that finds the longest common substring of two strings, and explain the trade-offs between using a brute force, a dynamic programming, or a suffix tree approach.

5 Testing and debugging

Another essential topic in algorithm analysis is testing and debugging. Testing is the process of checking if an algorithm works as expected and meets the requirements, while debugging is the process of finding and fixing errors or bugs in an algorithm. You should be able to design and perform various types of testing, such as unit testing, integration testing, functional testing, and performance testing. You should also be able to use various tools and techniques for debugging, such as breakpoints, print statements, logging, and debugging tools. For example, you might be asked to test and debug an algorithm that finds the shortest path between two nodes in a graph, and identify and correct any logical or syntactical errors.

6 Problem-solving and communication

The last but not the least topic in algorithm analysis is problem-solving and communication. Problem-solving is the skill of finding and applying solutions for problems, while communication is the skill of expressing and exchanging information clearly and effectively. To demonstrate your problem-solving and communication skills in an algorithm developer interview, it is best to understand the problem, explore possible solutions, implement the best one, verify it, and explain it. Ask clarifying questions, restate the problem, identify the input and output, make assumptions if needed, brainstorm possible solutions, analyze their pros and cons, write clear and concise code with meaningful variable names, comment your code, follow coding standards, test your code with different inputs, check for edge cases and errors, optimize your code if possible, walk through your code, explain your logic and reasoning, and answer any questions. By following these steps you can show your interviewer that you can not only write algorithms but also analyze them effectively.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?