Real-world algorithm practice

1. Real-world algorithm practice

Efficient algorithms make instant online shopping results and Google searches possible. Here, we'll compare Bubble Sort with Quick Sort and Linear Search with Binary Search to show how they impact our digital experience. The point is to gain an intuition as to why algorithm efficiency is important.

2. Commonly used algorithms: sorting & searching

Sorting and searching are common and essential functionalities in the real world. We've likely used them before, such as when sorting online shopping results by price or opening our calendar to see a sequence of our schedule. Searching is exactly as implied and is crucial in online shopping and customer support. If these algorithms were inefficient, results would take minutes to appear, making products or support unusable.

3. Sorting algorithms: bubble sort vs quick sort

Let's dig deeper into sorting and compare two algorithms and the efficiency differences. Bubble Sort is an easy-to-understand method for sorting a list. It works by looking at pairs of items next to each other and swapping them if they are in the wrong order. This process is repeated until the whole list is sorted. The time complexity of this algorithm is O(n^2). For large datasets, like millions of items in an online shopping catalog, it becomes inefficient. New items in the catalog will increase time exponentially. In contrast, Quicksort is a divide-and-conquer algorithm that works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The sub-arrays are then sorted recursively. This process continues until the entire array is sorted, typically resulting in an efficient average-case performance of O(n log n). Quick Sort is more sustainable for large-scale sorting tasks, such as sorting items in a massive online catalog.

4. Searching algorithms: linear search vs binary search

The goal of searching is to find relevant items as fast as possible. Linear Search checks each element one by one, with a time complexity of O(n). As the catalog grows, the search time increases linearly. In contrast, Binary Search is more efficient with a time complexity of O(log n), but it requires the list to be sorted first. This makes Binary Search much more efficient for large datasets.

5. Conclusion

We explored four key algorithms: Bubble Sort, Quick Sort, Binary Search, and Linear Search. Bubble Sort and Quick Sort are used for sorting, with Bubble Sort being simple but less efficient, and Quick Sort being faster and more scalable. Binary Search efficiently finds an item in a sorted list by dividing the search space, whereas Linear Search sequentially checks each element. Understanding these algorithms gives us insight into how different approaches affect the performance of the tools and products we use daily.

6. Let's practice!

Now, let's do a real analysis and compare the difference when using inefficient algorithms vs. efficient algorithms.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.