Here's how you can enhance algorithm efficiency in computer engineering with logical reasoning.

For embedded systems consider internal bus usages, interrupt / task priorities, looping mechanisms, instructions for implementations. Especially in multi-core embedded systems. You have a whole list of things that can contribute to bottlenecks that aren't obvious. Knowing where the data is located for any given implementation and where that code is running. Does it go out on a data bus? is it local to the core? going to some other memory on the bus that all cores use? Looping mechanisms always consider what you are looping on and worst case scenario for it. Does it also need a timer on that loop so it doesn't turn into an infinite loop (waiting on hardware maybe)? You can then test the worst case to make sure it won't cause a bottleneck.

Optimizing logic in algorithms involves using efficient data structures and algorithms to reduce computational effort. For Example, in a banking app, if you need to find a specific account transaction in a sorted list of transactions, instead of using a linear search (O(n) complexity), you can use a binary search (O(log n) complexity). This can reduces the number of comparisons, so improving efficiency.

On embedded systems debuggers will allow you to see the instruction breakdown and you can get that from the compiler even. Manuals will have timing breakdowns of instructions for you to use when optimizing code. A lot of times I may code up 2-3 solutions to a feature and see which one produces the best set of instructions for efficiency. Over time you learn for the compiler what way of coding is most efficient, it does change based on compiler. Make sure to not do things repetitively that may not be needed. Example, let's say you need to enable / write to some global registers. You don't need to write to them for every channel of that peripheral, just do it once. I've seen this in code reviews more times than you'd think.

You should always look at reducing complexity but not at the cost of functionality / re-usability of code. This is where code reviews are essential. Having people ask questions about how your code works or why you are doing a specific thing can help you to re-analyze your code and reduce complexity. It causes you to have to explain why you are doing it and realize it may not be needed.

Enhancing algorithm efficiency in computer engineering often involves reducing complexity. This can be achieved through logical reasoning by: Identifying inefficiencies: Analyze the algorithm to pinpoint operations or steps that cause unnecessary delays or consume excessive resources. Applying optimization techniques: Implement strategies like divide and conquer, which breaks a problem into smaller, more manageable parts, or dynamic programming, which stores intermediate results to avoid redundant calculations. By applying logical reasoning to systematically reduce complexity, you can significantly improve the performance and efficiency of computer algorithms.

Enhancing algorithm efficiency in computer engineering often involves reducing complexity. This can be achieved through logical reasoning by: Identifying inefficiencies: Analyze the algorithm to pinpoint operations or steps that cause unnecessary delays or consume excessive resources. Applying optimization techniques: Implement strategies like divide and conquer, which breaks a problem into smaller, more manageable parts, or dynamic programming, which stores intermediate results to avoid redundant calculations. By applying logical reasoning to systematically reduce complexity, you can significantly improve the performance and efficiency of computer algorithms.

I am a huge fan of refactoring code. You may write some code that solves a particular solution for a specific set of hardware / configuration. Then you start working on the next version / iteration of that product and get new hardware. A lot of times the original design may not be fully adaptable but the logic still stands for re-using the logic. Code should be refactored to be made better but to also allow for re-use and short comings that may be identified.

When considering / discussing complexity of an algorithm of solution don't confuse it with lack of knowledge / skillset of a developer. There are many times developers may think something is complex but it's because their skillset isn't there yet to understand code. Many people have a hard time with pointers and pointer arithmetic but that doesn't mean you should not use them because people think they are "complex"/"difficult".