# Sliding Windows: An Efficient Algorithmic Technique
The sliding window technique is a powerful algorithmic approach used to efficiently solve problems involving arrays or sequences by maintaining a “window” of elements and sliding it through the data. This method helps reduce time complexity, often from O(n²) to O(n), making it ideal for problems requiring subarray or substring analysis.
## How It Works
1. Define the Window: A fixed or dynamic range within the array/string.
2. Expand or Shrink: Adjust the window based on conditions (e.g., sum, uniqueness).
3. Track Results: Update the solution (e.g., max/min length, target sum) as the window moves.
## Common Use Cases
– Fixed-Length Window: E.g., find max sum of `k` consecutive elements.
– Variable-Length Window: E.g., find the longest substring with no repeating characters (as in the “Longest Substring Without Repeating Characters” problem).
## Benefits
– Efficiency: Avoids nested loops, optimizing runtime.
– Simplicity: Clean and intuitive implementation with two pointers.
### Example Code (Variable Window):
“`python
def longest_substring(s: str) -> int:
char_set = set()
left = max_len = 0
for right in range(len(s)):
while s[right] in char_set:
char_set.remove(s[left])
left += 1
char_set.add(s[right])
max_len = max(max_len, right – left + 1)
return max_len
“`
By mastering sliding windows, you can tackle many array/string problems with optimal performance. 🚀