How can you explain your coding choices in an Algorithms challenge?

When I approach a problem, my initial step involves a comprehensive grasp of its intricacies. By pinpointing the problem's specifications, I delve into potential solutions, leveraging algorithms and intricate design patterns. This focused understanding enables me to unearth diverse scenarios and intricate edge cases, paving the way for crafting code with optimal efficiency and precision.

When given a problem to solve, a good way to make sure you understand the problem is that write out your problem formulation in your own words, with all the necessary constraints, objectives, assumptions, etc. That way, you can make check whether your understanding of the problem is indeed correct and in line with the problem posed in the challenge, as well as end up with a concise summary of the problem that you can use to plan your solution.

I rephrase the problem statement to validate my interpretation. Clarifying questions uncover hidden assumptions - are inputs sorted or randomized? Walking through examples tests expectations - if all values are 0, what should the output be? These discussions align understanding from the outset, preventing wasted effort. I summarize edge cases that need handling to highlight thoroughness. Pseudocode sketches high-level logic for early feedback. This upfront alignment demonstrates communication and analytical skills vital for real-world success. Thoughtful preparation builds a solid foundation on which to efficiently construct quality solutions.

First step is to understand the problem clearly. What is the exact problem to be solved? In most of the problems, you have to consider all statements given in the problem to cover all constraints. Before jumping into the thought of solution, consider the problem alone and think over it to understand it in full clarity. Test your assumptions by considering edge cases. Try to find sample input and output that cover the edge cases.

In my experience, having encountered similar problems in the past, during the interview preparation phase or in your previous work experience helps to understand the problem better and provide a suitable solution. After all the reason why we practice and learn is so that the knowledge gained will be useful when the need arises.

Occasionally, encountered challenges are unprecedented, lacking pre-existing solutions through established algorithms. When faced with such scenarios, the imperative arises to craft an innovative algorithm tailored to address the unique problem at hand. This mandates a departure from conventional approaches, necessitating the inception of a novel algorithmic solution that not only tackles the specific intricacies of the uncharted problem but also contributes to the expansion of computational knowledge and problem-solving methodologies. In these instances, the essence of creativity and adaptability in algorithm design becomes paramount, paving the way for inventive solutions and advancements in the field

In algorithmic challenges, I prefer a modular, iterative approach. Initially, I opt for a simple method, like brute-force, to quickly grasp the problem and establish a working baseline. Then, I refine this solution incrementally, improving efficiency and handling complexities. This method ensures a maintainable and adaptable code structure, facilitating easier debugging and future enhancements. It's about evolving the solution progressively while maintaining clarity and effectiveness. This approach not only streamlines development but also ensures a robust, scalable solution, adept at handling varied scenarios with ease. It emphasizes gradual optimization, balancing immediate understanding with long-term code health.

Algorithm Selection: Clearly state the algorithm you chose to solve the problem (e.g., greedy, dynamic programming, divide and conquer) and explain why you chose it. If applicable, mention any data structures (arrays, linked lists, trees) you decided to use.

In my experience, people are one-track-minded and severely bias towards for-cycles and classes. I hate OOP and I save my for for the rare occasion when I viusit good old C. I am a Functional First person, so I go for declarative and no-side-effects approaches. I'm talking about my two babies: Python and JavaScript. A thing that I often find frustrating, maybe due to the amount of non-software-engineering people writing code these days, is that Big-O seems to be thought of as not applying to built-in functions. In Python, for instance, any method for searching on a list is assumed to be instantaneous, and therefore not taken into consideration when analysing O complexity.

You know what, people with old ideas will always gives you the advice of unwrapping the question first, but I feel that firstly you have to look for testcases, if it clicks some hints then it will save your time in reading question . That is how, you can lead in hackathon and other competitive programming . Because the statement is given long sometimes, just to waste your time.

Before I write the actual code, I write pseudocode to outline the main steps of the algorithm. Explaining each step in plain English, and using comments to describe the variables, functions, and data structures. Sometimes I use diagrams, tables, or graphs to visualize the algorithm. It shows a structured plan, and that you can break down the problem into smaller and simpler subproblems. I write the code for the algorithm. Pick a programming language that you are comfortable with, and follow the coding style and conventions of that language. Explain the syntax and semantics of your code, and how it implements the pseudocode.

I first write out the logical steps in plain language using simple commands like "input", "output", "loop". Add brief explanations of the operations and data flows. Basic diagrams visualize the macro flow between modules. This structured thinking enables tackling complexity in chunks. I can focus on logical cohesion before optimizing code efficiency. Pseudocode acts as a blueprint for translating intent into syntax. It also allows reviewing the algorithm logic with colleagues to get feedback. With an abstract design solidified, I can then work top-down writing actual code for each block.

If you struggle with this step (and many people do have blockages on how to start) you can write simple sentences to help put you on a good track.

I've been doing this since 1994. Excuse me if I do the pseudo-code thing in my head and save time and ink / keystrokes. I admit to the occasional list of comments that are then progressively implemented, but forcing pseudo-code as a mandatory step for complying to whatever New Age conception of "Clean Code" might be trending this week is what I expect of newcomers and the kind of people I mentor. Colour me proud, reckless, conceited and vane.

Before I write the actual code, I write pseudocode to outline the main steps of the algorithm. Explaining each step in plain English, and using comments to describe the variables, functions, and data structures. Sometimes I use diagrams, tables, or graphs to visualize the algorithm. It shows a structured plan, and that you can break down the problem into smaller and simpler subproblems. I write the code for the algorithm. Pick a programming language that you are comfortable with, and follow the coding style and conventions of that language. Explain the syntax and semantics of your code, and how it implements the pseudocode.

I choose a language well-suited to the problem domain - R for statistics or Python for automation. Layout, naming conventions and comments make logic clear. Descriptive names like user_dataframe rather than user_df ease understanding. Modular functions keep code intention focused. Comments explain tricky implementations or optimizations. I refine iteratively ensuring the code reliably executes the pseudocode flow and handles edge cases. Well-structured code allows future edits without unraveling complexity. Careful programming balances human-readability for colleagues while enabling computer-readability for smooth execution. With robust practices, the code brings the validated algorithm design to functional life in a collaborative way.

Language adoption is very often not a choice, especially when we join an ongoing project. A couple years ago, I started working on a medical device where automation was made in Python. We're talking device, hardware, PLC automation. The choice was evidently bad and the immediate future revealed it very painfully. I am extremely comfortable with Python and JS. However, my comfort is second to the convenience of the project and I would rather learn a new language than adapt the problem to my own skills. Adjusting the problem to a predetermined solution (like Python, or OOP, or NoSQL for SQL-like data) is the dumbest possible option and one that will not wait long to come back bite you in the arse with vengeance.

Everyone will say that, don't rush to code. But I totally disagree with this because it internally gives you ,the push to finish the question in less time . For me it works , also it helps me to focus sharply on the question without being distracted.

For the past 3 years, I've been working in environments without a few things. Without Agile and without Jira are two massive improvements, as the former is yet another great example of a "the King is naked" trend where everybody ius doing it because... everybody is doing it. Glorious waste of time. However, I've also been working in an environment where pytest is a word from a foreign language. This is very very wrong. We simply cannot not test these days, and not because it's a trend. Rather because projects are bigger and bigger, with more and more devs involved and each stage amplifies the bugs, assumptions and errors of the previous. Think of doing ML without cleaning data first. Yeah, right.

When testing, I review examples, special situations, and corner cases to ensure the solution works well. If there are different ways to solve the problem, I might talk about them and explain why I picked the one I did. This helps ensure that the solution is solid and reliable.

Talking clearly and responding well to the interviewer's comments are essential for me to show how I approach and solve problems. It's a way for me to demonstrate my skills in problem-solving and thinking through algorithms.

It might not always Time Complexity, but space complexity could also be constraint. So, always gather non functional requirements as well clearly and then choose the approach

Think that there is a chance your code will be reshared or QA by a colleague and, out of respect and courtesy, try to produce a piece of code you would like to receive if your roles were swapped. I found out that many times this simple exercise helps me to write clearer and more efficient code.