How can you structure algorithm documentation for clarity?

When structuring an algorithms document, it's important to clear about the three main steps Defining the Problem and How it is a problem The ways we can solve it Final approach and why we are using it instead of others

Address real-world problems with AI and blockchain-enhanced algorithms. Utilize cloud services and AI for efficient, scalable algorithm strategies. Analyze complexity with cloud scalability and distributed computing's impact. Validate algorithms using AI-driven tests and CI/CD in cloud settings. Discuss AI and blockchain's current limits and areas for efficiency improvements. Highlight the algorithm's performance and applicability through AI and cloud integration. Emphasize evolving algorithm design with AI and DLT, focusing on adaptability.

When structuring an algorithms document, it's important to define the problem in a clear and structured manner. Start by providing a concise but comprehensive problem statement that describes what the algorithm aims to solve. Include details such as the input format, any constraints or assumptions, and the desired output. This helps readers understand the context and scope of the algorithm.

To define a problem, it's always better to start with a diagram and then move to pseudo codes. This helps to explain the problem both in terms of non-technical people and also from a technical perspective since pseudo codes require coding logic with a few exceptions. There are other things that one can use if time permits. That is already discussed in this article.

While using varied formats like natural language, pseudocode, or diagrams facilitates comprehension, ensuring clarity and simplicity is paramount. Avoiding technical jargon and ambiguity allows for a broader audience to understand and engage with the algorithm documentation. Additionally, clarity in defining assumptions and constraints enables better understanding and application of the algorithm across different scenarios or by various stakeholders. This approach ensures that the documented algorithm is not just comprehensive but also accessible and easily applicable within diverse contexts.

In understanding the problem, the methodology used to develop the algorithm can vary. The analysis of the problem is equally as important as a clear expectation of what the algorithm is will produce. Some levels of reverse engineering may be employed to reinforce completeness of the solution.

Setting this up provides clarity on the goal for anyone else going through it to understand the logic and reasoning behind the functions used in designing the algorithm. This is also for your benefit, even if it might seem unnecessary at the time because the reason for the algorithm is in your head. However, in a day or two you'd start to lose clarity on why you did what you did and if you have this description properly set up, you wouldn't need to spend more time breaking down the algorithm and trying to remember your reasoning behind it.

After choosing the approach, keep these things visible: 1. Overview of path between input to output 2. Use descriptive naming 3. Each root component must be simple without hidden/changing functionality I always believe that code must comment itself. We are building things that often outlast ourselves. Readibility is important, 80% of our time is spent reading than writing the code.

In my approach to algorithm analysis, a critical step is assessing the complexity and efficiency, considering both time and space factors. This involves offering a mathematical or empirical estimation of how the algorithm scales with varying input sizes, and how it stacks up against alternative solutions. Utilizing asymptotic notation, particularly Big O, allows for expressing worst-case, average-case, or best-case scenarios. Supporting this analysis with examples or graphs provides a visual and practical dimension to the assessment, enhancing the clarity of the algorithm's performance characteristics.

Unless you are a researcher, avoid over-analyze or early optimisation. We have limited budget and time, optimum solution must consider the delivery and business impact. I have seen a lot of cases of over thinking and over designing that actually doesn't support future use cases. Let the code be always unfinished business.

- Clearly define "correctness". It might varies per use case. - Build test data based on real life for each use case. - Estimate thresholds of error. - Define category and employ metrics. - Organise sample result across all categories. - Show breaking points wherever possible.