sliding_window

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Sliding Window Algorithm Patterns

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Concept

The sliding window pattern uses a subset (or window) that moves across the data structure, typically arrays or strings, to keep track of a specific condition (like max, min, sum, or unique elements) over contiguous elements. It’s great for reducing nested loops (O(n²)) into single-pass solutions (O(n)) by efficiently expanding or shrinking the window.

When to Use

• When you need to process contiguous subarrays or substrings.
• When tracking sums, counts, or properties within a fixed or variable-sized window.
• When optimizing for minimal/maximal subarray results without reprocessing the entire window each time.

Common Operations

• Expanding the window by moving the right pointer forward.
• Shrinking the window by moving the left pointer when a condition is exceeded.
• Using a hashmap, set, or counter to track elements inside the window.

Examples

#	Name	Java	Python	Go	Link	Company
1	Maximum Average Subarray I	✅	✅	❌	LeetCode #643
2	Longest Substring Without Repeating Characters	✅	✅	❌	LeetCode #3
3	Longest Repeating Character Replacement	✅	❌	❌	LeetCode #424
4	Minimum Window Substring	✅	❌	❌	LeetCode #76
5	Permutation in String	✅	✅	❌	LeetCode #567
6	Quadratic Consecutive Sequence Sum	✅	✅	❌		#TrendMicro
7	Distance Between Scrambled Programmer Words	✅	✅	❌		#ExpediaGroup

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