Sliding Window in 7 minutes | LeetCode Pattern

Summary

The video introduces the sliding window technique, a powerful approach for efficiently solving problems involving arrays and strings. It explains the two main types of sliding windows: fixed and dynamic, with examples of both. The video also walks through how this pattern reduces time complexity and provides solutions for popular problems like 'Maximum Average Subarray' and 'Longest Substring Without Repeating Characters.' By applying the sliding window, problems that would take O(n^2) time can be optimized to O(n), making it a valuable tool for coding challenges.

Takeaways

• 😀 Sliding window is a powerful problem-solving pattern using two pointers to find subarrays or substrings in an array or string.
• 😀 The sliding window technique can reduce time complexity from O(n^2) to O(n) in many array and string-related problems.
• 😀 There are two types of sliding windows: fixed sliding window and dynamic sliding window.
• 😀 A fixed sliding window maintains a constant window size and is useful when the required size is known in advance.
• 😀 A dynamic sliding window expands or shrinks based on conditions and is used for problems with variable window sizes.
• 😀 The sliding window pattern helps solve problems efficiently, such as finding subarrays or substrings that meet certain conditions.
• 😀 In the fixed sliding window, the window size is constant, and the algorithm tracks the result by adding and removing elements as the window slides.
• 😀 For dynamic sliding window problems, the window adjusts based on the condition being evaluated, such as finding the longest substring without repeating characters.
• 😀 A key idea in sliding window is to dynamically update the window state (e.g., sum, frequency) as elements enter and leave the window.
• 😀 Examples of LeetCode problems solved with sliding window include the maximum average subarray (fixed window) and longest substring without repeating characters (dynamic window).

Q & A

What is the sliding window pattern?

The sliding window pattern is a technique used to efficiently process a subset of data in problems involving arrays or strings. It uses two pointers to define a window that slides over the data structure to find subarrays or substrings that meet certain conditions.

How does the sliding window pattern help reduce time complexity?

By using two pointers to define a window that moves over the data, the sliding window pattern allows for more efficient processing of data. It reduces the time complexity from O(n²) in brute-force solutions to O(n) in many cases, especially in array- and string-related problems.

What are the two main types of sliding windows?

The two main types of sliding windows are: 1) Fixed Sliding Window, which maintains a constant window size and is used when the window size is known in advance, and 2) Dynamic Sliding Window, where the window size expands or contracts based on the conditions of the problem.

When would you use a fixed sliding window?

A fixed sliding window is used when the required window size is known beforehand, and the problem asks for subarrays or substrings of that fixed size. For example, problems like finding the maximum sum of a subarray of fixed size K can be solved using this approach.

How does the dynamic sliding window work?

The dynamic sliding window is applied when the window size is not fixed. The window expands by moving the right pointer and contracts by moving the left pointer when the window violates a condition (e.g., encountering a duplicate character or exceeding a sum limit).

What is the time complexity of the sliding window approach?

The time complexity of the sliding window approach is generally O(n), where n is the length of the data structure (array or string). Each element is processed at most twice—once when it is added to the window and once when it is removed.

Can you give an example of a problem solved with a fixed sliding window?

An example is LeetCode 643, where you need to find the maximum average of any contiguous subarray of size K. Using a fixed sliding window, we can calculate the sum of the first K elements, then slide the window across the array, updating the sum dynamically.

What is a dynamic sliding window used for?

A dynamic sliding window is used for problems where the window size is not fixed, and the goal is to find the longest or shortest subarray or substring that satisfies a condition, such as a string without repeating characters (LeetCode 3).

How can the sliding window pattern optimize the longest substring without repeating characters problem?

Instead of checking all possible substrings, the sliding window expands the window by moving the right pointer when all characters are unique. If a duplicate is found, the left pointer is moved to shrink the window and remove duplicates, ensuring a more efficient solution.

What are some additional problems where the sliding window approach can be applied?

Other problems where the sliding window approach is applicable include finding the longest substring with at most two distinct characters, determining the minimum window substring that contains all characters of another string, and various problems related to maximum sum subarrays or substrings.