Sliding Window Algorithm: Explained with Example

The Sliding Window Algorithm is among the various other algorithms that are crucial to learning due to its use cases in the range of problems that occur. In this article, we’ll look at the fundamentals of the Sliding Window Algorithm and solve problem that get optimized with the Sliding Window Algorithm.

What is Sliding Window Algorithm?

The Sliding Window Algorithm is an optimization approach used for efficiently processing arrays, strings, or other data structures. The basic idea is to maintain a ‘window’ of elements within the data, and as you iterate through it, you slide the window to cover the next set of elements. This technique proves to be particularly powerful in scenarios where you need to find a subset of data that meets certain criteria.

You can consider an array of integers and the task of finding the maximum sum of a subarray of a given size. Instead of recalculating the sum for each subarray from scratch, the sliding window keeps track of the sum as it moves through the array which efficiently reduces the time complexity of the operation.

Sliding Window Algorithm Walk Through

Let’s take an example of a list of integers i.e, [1,2,3,4,5,6]

Now, we want to take a window size of 2, which means that we will be covering two blocks of adjacent integers at a time, here’s a visual representation of the sliding window algorithm.

Key Components of a Sliding Window Algorithm

1. Window Initialization: The first step in implementing a sliding window algorithm is to initialize the window. This involves setting the starting and ending points based on the requirements of the problem.
2. Processing Elements: As the window moves, it processes elements within its boundaries. The nature of this processing depends on the specific problem. It might involve calculations, comparisons, or any other operation relevant to the task at hand.
3. Window Movement: The window’s movement is an important aspect of sliding window algorithms. It dictates how elements are included or excluded from the window as it traverses the array.

Optimizing Time and Space Complexity

One of the key strengths of the Sliding Window Algorithm lies in its ability to optimize both time and space complexity. As it maintains a constant-size window as it traverses the dataset, the algorithm avoids redundant computation which leads to a more efficient solution. This is particularly advantageous when dealing with large datasets or real-time data streams where performance is very important.

Furthermore, the sliding window often eliminates the need for additional data structures which helps us reduce the overall space complexity of the algorithm. This minimalistic approach contributes to its elegance and effectiveness in solving a wide range of problems.

Common Variations of the Sliding Window

As with many algorithms, the Sliding Window Algorithm comes in different variations tailored to address specific types of problems. One such variation is the Fixed Size Sliding Window, where the window size remains constant throughout the traversal. This is useful in situations like the maximum subarray sum problem.

Another variation is the Variable Size Sliding Window, where the size of the window dynamically adjusts based on certain conditions. This flexibility is particularly beneficial when dealing with problems that involve finding variable-length patterns or subsets within the data.

When to use Sliding Window Algorithm

The sliding window algorithm is best leveraged in situations that meet a few key criteria:

1. Sequential or Time Series Data: The data arrives in the form of a long continuous sequence or history over time. For example, log streams, sensor readings, and video frames.
2. Need for Incremental Real-Time Processing: The processing task needs to happen continuously in real-time as new data arrives rather than batch post-processing.
3. Repeated Expensive Calculations: Naive algorithms end up redoing the same complex and resource-intensive computations over and over unnecessarily as the sequence evolves.
4. Locality of Reference: Computation results tend to exhibit strong locality – meaning only recent context is needed rather than all historical data.
5. Memoization Benefits: Intermediate results of processing sub-sequences can be cached or stored temporarily to avoid recomputing from scratch.

Sliding Window Algorithm Python Example

Here is one of the most common DSA questions: Finding the maximum sum of a subarray of a given size. Let’s try solving this problem with python.

`
def max_subarray_sum(nums, k):
max_sum = float('-inf')
current_sum = 0

for i in range(len(nums)):
    current_sum += nums[i]

    if i >= k - 1:
        max_sum = max(max_sum, current_sum)
        current_sum -= nums[i - (k - 1)]

return max_sum

print(max_subarray_sum([4, 5, 6, 7, 1, 4, 9, 0, 3, 1, 7], 3)) # Output: 18
`

In the above-written python code, we wrote a function max_subarray_sum() that takes two parameters: nums and, k which are the list and size of the maximum subarray. We initialized our max_sum variable with the minimum float value and current_sum as 0 which will calculate our sum in every window.

Then, we run a for loop through the list and add the current element value i.e, nums[i] to the current_sum variable. After that, we check, if the value of our index (i) is greater than the 1- the given size of our window (k), if it is true then it means that the current window size is equal to the given window size in question hence we assign the max_sum variable the maximum of our max_sum and current_sum values.

We would also have to traverse our window so in order to do that we need to remove 1st element from our window, hence we do current_sum -= nums[i-(k-1)] and we return our max_sum.

Originally Published at: https://pypixel.com/

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