Monotonic Stack Pattern: LeetCode Guide & Problems
Maintain a sorted stack to find next/previous greater or smaller elements in O(n).
Problems (7)
| Problem | Solution | Link | |
|---|---|---|---|
| Largest Rectangle in Histogram
#Array
#Stack
#Monotonic Stack | Solution | ||
| Next Greater Element I
#Array
#Hash Map
#Stack | Solution | ||
| Sum of Subarray Minimums
#Array
#Dynamic Programming
#Stack | Solution | ||
| Online Stock Span
#Stack
#Design
#Monotonic Stack | Solution | ||
| 132 Pattern
#Array
#Binary Search
#Stack | Solution | ||
| Remove Duplicate Letters
#String
#Stack
#Greedy | Solution | ||
| Stock Span Problem
#Stack | Solution |
Mastering the Monotonic Stack Pattern for Coding Interviews
1. Mental Model & Visual Intuition
The Monotonic Stack pattern is designed to eliminate redundant work and reduce search spaces. Rather than evaluating every possible state using a naive brute-force approach, this pattern relies on an invariant that allows you to safely discard subproblems that cannot lead to an optimal answer. When encountering an interview question, look for core triggers such as monotonicity, contiguous constraints, optimal substructure, or overlapping subproblems.
2. Algorithmic Invariants & Step-by-Step Execution
To implement this pattern flawlessly under interview conditions, structure your thought process into three discrete steps:
- State Initialization: Define your boundaries, pointers, or memoization tables clearly before entering loops.
- State Transition: Update tracking variables incrementally as new elements enter or leave your active evaluation window.
- Feasibility Check: When constraints are violated, systematically shrink or adjust your state until the valid invariant is restored.
3. Time and Space Complexity Trade-Offs
In top-tier technical interviews at Google, Meta, and Amazon, arriving at a working solution is only half the battle. You must explicitly justify why your approach achieves optimal Big-O bounds. For Monotonic Stack, the target time complexity is typically O(n) — each element pushed/popped once while maintaining an auxiliary space complexity of O(n). Always discuss whether an in-place modification or two-pass traversal can save memory.
4. Common Interview Edge Cases to Test
Before informing your interviewer that you have completed coding, trace your logic against these standard high-failure test cases:
- Empty inputs, single-element collections, or all-identical values.
- Inputs with negative numbers, duplicates, or extreme values that could cause integer overflow.
- Odd versus even length inputs when dealing with pointer convergence or tree midpoints.
Related Guide
Monotonic Stack — Complete Guide With LeetCode Problems [2026] →Monotonic stacks solve next-greater-element and histogram problems in O(n). The pattern, both varian…