Key Ideas About How to Create Algorithms

1. Correctness: An algorithm should solve a problem correctly every time, no matter what information you give it. It’s important to test algorithms well. Did you know that most programming mistakes—about 80%—come from logic problems? That’s why checking your work is super important.

2. Efficiency: Algorithms need to use resources wisely. This means:

   • Time Complexity: This shows how long an algorithm takes to run as the size of the input grows. We often use something called Big O notation, like $O(n^2)$ for bubble sort. It helps us understand how the time needed increases when there’s more data.
   • Space Complexity: This tells us how much memory an algorithm uses. This is really important when you don’t have a lot of resources. For example, quicksort uses memory more efficiently, with a space complexity of $O(\log n)$.

3. Scalability: A good algorithm should still work well even when the amount of data gets bigger. Studies show that sometimes an algorithm can slow down by 10 times or more when the input size doubles. So, it’s important to design algorithms that can grow with the data.

4. Simplicity: Algorithms should be as straightforward as possible. This makes them easier to understand and fix later. Many simpler algorithms work better in real life than complicated ones. In fact, about 70% of developers say they prefer algorithms that are easy to read instead of just fast.