Here's how you can analyze and optimize algorithms effectively during an interview.

I did an interview where I was asked to analyze code; I said I'm an EE not a CS grad, I just fix things. We spent 25 minutes discussing the code; I then fixed it in 5 using standard coding techniques. This is a "technical debt" issue: do you have time for full analysis, or should you just fix what's in front of you. IMO, if you do enough fixing, you'll work out what the better thing to do is eventually. You'll also learn how to write code with the flexibility to do that quickly. Things like the C++ standard template library commit you to doing things a particular way, and I view STL and being asked to do algorithmic analysis as antipatterns in a software job.

1. Understand the Basics: Ensure you grasp fundamental data structures like arrays, trees, graphs, linked lists, stacks, and queues. This foundation is crucial for solving complex problems. 2. Know Your Algorithms: Analyze the problem statement to determine the appropriate algorithm, whether it's dynamic programming, DFS/BFS, greedy methods, or binary search. 3. Start with Brute Force: Begin with a basic brute force solution to gain a better understanding of the problem, which can help identify ways to optimize your solution and choose your final algorithm. 4. Think Aloud: Verbalize your thought process. This helps the interviewer understand your reasoning and problem-solving approach, demonstrating a structured and methodical mindset.

1. Focus to get the most naive approach first. Once you get it, getting to solution will be just like connecting the dots. 2. Check the areas which needs optimizations and can be optimized, keeping the space and time trade-offs in mind. 3. Don't keep the edge cases for end, Edge cases can help pave the entire logic in many cases.

Revisiting constraints helps identify feasible solutions by understanding input size and limits. Finding special cases (e.g., empty input, duplicates) ensures your solution handles edge cases effectively.

We have various data structures and algorithms to meet different needs. Knowing many data structures is important, but understanding how to approach them is even more crucial. Data structures have different metrics, such as insert/delete/read/sort time complexity and the types of data they can store. One technique that helped me solve problems, was determining the necessary metrics for the solution and then searching for the appropriate data structure.

Time and space complexity are crucial factors to consider when analyzing a problem and framing it within an algorithmic framework. Additionally, it's important to evaluate whether the most common instances of the problem can be effectively handled by an approximation algorithm, which could offer significantly faster runtime compared to an exact solution. We aim to explore how frequently instances that can be solved by an approximation (with an arbitrarily small error) occur in real-world scenarios.

Don't rush to solve! Take your time to fully understand the problem. Ask questions and clarify constraints. A true expert takes the time to get it right, rather than rushing to a solution and that's what makes them different. Remember, understanding the problem is just as important as finding a solution.

To Come arrive to particular solution very essential and mandatory thing is neet understanding of problem that you need to solve. once you understand the problem then you can select appropriate algorithm design strategy to devise an algorithm. you need to follow tested driven development so that you can come up with optimal algorithm

Choosing the right algorithm depends on the specific requirements of the problem. For sorting, quicksort is often preferred for its average-case time complexity and in-place sorting, but mergesort is better for guaranteed stability and worst-case performance. Analyze input size, stability needs, and memory constraints to decide appropriately.

Le choix d'un algorithme adapté doit être rapide cela doit être un réflexe. Il faut donc avoir dans sa tête les mécanismes, les points faibles et forts, son adaptabilité, sa rapidité afin de répondre efficacement aux défis du problème.

Pseudocode is like a roadmap for coding. It outlines the steps to solve a problem without getting bogged down in programming syntax. It helps organize thoughts, spot potential issues, and plan the solution's flow. Think of it as a blueprint for your code.

Optimizing code involves streamlining processes to enhance efficiency. Strategies include eliminating redundant computations, reducing complexity through single loops instead of nested ones, and implementing memoization to store and reuse expensive function results. Discussing these optimizations during interviews demonstrates critical thinking and proficiency in coding.

L'optimisation, le refactring du code et le point de vue des collègues, des experts sont important pour mieux accomplir la tache.

Don't forget about corner cases! These unexpected inputs can easily slip through the cracks, but they can make all the difference in the reliability of your algorithm. Be sure to test for extreme values, null or empty inputs, and other unusual scenarios that might not be immediately apparent.

Tester son algorithme, sa vitesse d'exécution, sa presision, sa robustesse, son occupation mémoire sont des éléments importants. Chacun des algorithmes que nous développons doit être testé par des Benchmarks et compréhensibles par tous.

I don't and I think a good interviewer shouldn't ask for that, unless your core skill is exactly that. I would ask them which one of their prior projects was their most successful and meaningful and best to describe their talent. Then I go deep and leave no room for BS.

This general rule has helped me not just in interviews but also in other aspects of problem analysis. Research supports its effectiveness: Talk to yourself about the problem (it doesn't have to be out loud). This approach helps you grasp the problem better, break it into smaller parts, identify challenges easier, and achieve better results. If you do this out loud during an interview, it can also demonstrate your analytical skills to the interviewer, which can be in your advantage.