Sliding Window Algorithm Visualizer
Interactive tool to master sliding window and two-pointer algorithms for coding interviews. Visualize fixed-size windows, variable-size windows, and "at most K" patterns step-by-step.
Maximum Sum Subarray
Find the maximum sum of any contiguous subarray of size K.
def max_sum(arr, k):
sum_val = 0
max_sum = 0
for i in range(len(arr)):
sum_val += arr[i]
if i >= k - 1:
max_sum = max(max_sum, sum_val)
sum_val -= arr[i - k + 1]
return max_sumUnderstanding Sliding Window Patterns
The sliding window technique is an optimization that reduces time complexity from O(n²) to O(n) for array and string problems. Instead of recalculating for every subarray, you maintain a dynamic window that slides through the data, updating state incrementally. This approach is essential for coding interviews.
Three Core Patterns
Fixed-Size Window
Window size stays constant throughout. Use for problems like maximum sum of K consecutive elements.
Variable-Size Window
Window expands and contracts based on conditions. Ideal for longest substring problems.
"At Most K" Pattern
Maintains a constraint like "at most K distinct characters" while maximizing window size.
Key Insight
All sliding window problems follow the same pattern: expand the window by moving the right pointer, shrink it when invalid by moving the left pointer, and update your result at each step.
How to Use the Visualizer
- Select a pattern type to load an example problem
- Use Next/Previous buttons to step through the algorithm
- Watch the left (L) and right (R) pointers move through the array
- Monitor state changes (sum, count, window size) in real-time
- Use Auto Play to see the complete execution
Practice Problems
Apply what you've learned with these LeetCode problems:
Learn More
Explore our comprehensive sliding window tutorials and guides:
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