You’re developing an algorithm. How do you ensure it’s the best it can be?

The first step is always to understand the scope of the problem you're trying to solve. By scope I mean: - Inputs - Expected outputs - Problem constraints - Efficiency (time & space) Once you have a clear picture of the scope, then ask yourself a few questions: 1. Can you think of a brute force solution? (we start small, okay?) 2. If so, can you think of a way to optimize it to the desired time and space efficiency. 3. Does your algorithm handle the edge cases of the problem? 4. Is the problem of optimal substructure? Can you break it down into smaller problems and solve those first? All these will help you understand what the problem requires and in turn design an efficient algorithm. Pro tip: Spend 15 minutes understanding & 5 coding.

After understanding the requirements of the problem, what follows is choosing an approach. Here are some examples: 1. The problem requires a linear runtime. - If that's the case, we know that our algorithm can't have nested loops(not always). - So, we are constrained to reason about algorithms that run in O(n). 2. Let us say the problem requires constant data lookups. - Then we use a hashmap because it supports that operation. 3. Or maybe the problem requires to find distances between nodes. - Then we know that we are to use graph algorithms. This is the point, the approach you choose depends on your comprehension of the problem requirements. You have a lot of tools in your toolbox, so your job is to match the problem to the right tool.

Follow these steps to assess the model and prioritise improvements: 1) Use one or two metrics to evaluate the algorithm, choosing the most relevant for your application. 2) Add some variations. You may compare the method against the available benchmark models. You may train for different amounts of data to see if the model continues improving as data increases. 3) Perform error analysis and data exploration. Look at the best and worst results to get feedback of what can be improved. 4) Write a list of the largest errors and possible solutions and order them in order of priority.

In my experience, to make an algorithm the best it can be, optimization is key. This involves carefully considering both time and space complexities to ensure efficiency and effectiveness. By analyzing and refining the algorithm, one can eliminate redundancies, streamline processes, and select appropriate data structures or techniques, ultimately enhancing its overall performance.

Optimizing an algorithm involves enhancing its overall performance, balancing time complexity, space complexity, and other relevant metrics. Minimizing execution time requires optimizing time complexity through algorithmic analysis and efficient data structures. Reducing memory usage entails focusing on space complexity through data structure optimization. Striking a balance between time and space complexity is crucial, considering trade-offs and overall performance objectives. Regular testing and benchmarking ensure optimization efforts meet desired performance goals and enhance the algorithm's efficiency.