Mastering the Art of Problem-Solving: The Sliding Window Algorithm

Reflecting on when I first began coding, finding a solution to a problem was a source of immense joy. It was satisfying to know exactly how to solve a problem and then bring it to life through programming. As I’ve gained more experience, however, simply finding a solution no longer brings the same level of fulfillment. I now strive to optimize my solutions, exploring different tools and methods to enhance my code’s efficiency.

As a developer, you will encounter algorithms and data structures frequently. One technique that can be incredibly valuable in your toolkit is the sliding window technique. This technique is especially useful for addressing array or string problems. In this post, we will explore the sliding window technique, how it works, and how you can use it to tackle problems.

What is the Sliding Window Technique?

The sliding window technique is an algorithmic approach that uses a window of fixed size to traverse a set of items. The window moves from left to right and is particularly useful for identifying subarrays or substrings that meet specific requirements. To use the sliding window technique, you need two pointers — one for the start of the window and one for the end. Initially, both pointers point to the first element in the collection. Next, we move the end pointer to the right while keeping the start pointer fixed. As soon as the end pointer meets a particular condition, we shift the start pointer to the right and repeat the process until we have traversed the entire collection.

Here’s an optimization problem that I recently tackled and learned a lot from. It’s Leetcode 643: Maximum Average Subarray I. The task is to find the contiguous subarray of length k in a given array, nums, that has the highest average value and return that value. To break it down, we need to divide the array into subarrays of length k, calculate the average of each subarray, compare it with the highest average, and return the maximum.

Let’s start with declaring two variables to keep track of the highest average and current sum.

Next, we iterate through the array and add the current element’s value to the sum variable.

During the kth iteration, we calculate the average of the current subarray, check if it is greater than the current highest value, reassign the highest variable if true, and decrement the sum by the first value of the subarray to move the window. We repeat this process until we reach the end of the array. For example, consider the input array nums = [1,12,-5,-6,50,3] and k=4. The first subarray is [1,12,-5,-6], with a sum of 2 and an average of 0.5. The next subarray is [12,-5,-6,50], with a sum of 51 and an average of 12.75.

After implementing this logic, we simply return the highest variable to get the solution. By optimizing our code using this technique, we have solved the problem with ease.

Let’s wrap things up, as developers, we constantly strive to improve our skills and optimize our code to enhance efficiency. The sliding window technique is a powerful algorithmic approach that can help us tackle real-world problems, especially when dealing with arrays or strings. By using this technique, we can identify subarrays or substrings that meet specific requirements, making it an effective tool for solving optimization problems like Leetcode 643: Maximum Average Subarray I. By breaking down the problem and implementing the sliding window technique, we were able to easily solve the problem with optimal efficiency. As we continue to learn and improve, let’s keep exploring different tools and techniques to enhance our problem-solving skills and become better developers.