Monotonic Stack & Queue Visualizer
Interactive tool to master monotonic stack and queue algorithms for coding interviews. Visualize Next Greater Element, Daily Temperatures, and Sliding Window Maximum step-by-step to understand the O(N) optimization.
Next Greater Element
Current State
def next_greater_element(nums):
n = len(nums)
result = [-1] * n
stack = [] # Stores indices
for i in range(n):
while stack and nums[i] > nums[stack[-1]]:
idx = stack.pop()
result[idx] = nums[i]
stack.append(i)
return resultUnderstanding Monotonic Stack & Queue
Monotonic stacks and queues are powerful techniques that solve specific range-query problems in O(N) time, down from O(N²) brute force. The core idea is to maintain elements in a specific order (increasing or decreasing) to efficiently find the "next greater" or "next smaller" element, or the maximum in a sliding window.
Three Core Patterns
Monotonic Stack
Maintains increasing or decreasing order. Used for finding next/previous greater/smaller elements (e.g., Next Greater Element).
Stack with Indices
Stores indices instead of values to calculate distances. Essential for problems like Daily Temperatures and Stock Span.
Monotonic Queue (Deque)
Supports adding/removing from both ends to maintain a sliding window maximum or minimum in O(N) time.
Key Insight
The magic of monotonic stacks is that each element is pushed onto the stack exactly once and popped at most once. This amortized analysis guarantees linear O(N) time complexity, regardless of the input distribution.
How to Use the Visualizer
- Select a pattern type (Next Greater, Daily Temps, Sliding Window)
- Use Next/Previous buttons to step through the algorithm
- Watch how the stack/deque grows and shrinks to maintain order
- Observe the resulting array being filled as relations are found
- Use Auto Play to see the flow of data
Practice Problems
Apply what you've learned with these classic LeetCode problems:
Learn More
Dive deeper into our complete guides for Monotonic Stack & Queue:
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