: Start by profiling the algorithm to find where the most time or resources are being consumed—often the "hot spots" like nested loops or frequent I/O operations. Example: In a data processing pipeline, if 80% of the time is spent sorting data, optimizing the sort operation (e.g., switching from O(n^2) to O(n log n) complexity) will yield the most significant performance gains. us on optimizations that directly improve the user experience, such as reducing latency in interactive components or speeding up response times for key features. Example: For an e-commerce platform, optimizing the search algorithm to return results faster can significantly enhance user satisfaction, especially during peak shopping times.

Here the most important thing is to understand the need, this will give you an idea of what is of high priority and what can come later. You can then prioritize on tasks what are needed the most and which gives more value. Once you have prioritized task, check on the usability of your algorithm, make sure you prioritize optimization which give better user experience. Your algorithms complexity is also governed with usability. Next is to create a template of your algorithm which will give you an idea of the bottlenecks that can come into picture. For example you have a set collection to be used and you want it to be sorted, instead of going for HashSet and calling sort on it directly go for TreeSet, this is performance efficient.