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Stack Pattern: LeetCode Guide & Problems

LIFO structure for matching brackets, evaluating expressions, and monotonic problems.

✓ Pattern Solutions & Guides Practice Mode Enabled
22 Problem 2 Easy 20 Medium 0 Hard

Problems (22)

Problem Solution Link
Valid Parentheses
#String #Stack
Solution
Min Stack
#Stack #Design
Solution
Evaluate Reverse Polish Notation
#Array #Math #Stack
Solution
Daily Temperatures
#Array #Stack #Monotonic Stack
Solution
Decode String
#String #Stack #Recursion
Solution
Asteroid Collision
#Array #Stack #Simulation
Solution
Remove K Digits
#String #Stack #Greedy
Solution
Car Fleet
#Array #Stack #Sorting
Solution
Basic Calculator II
#Math #String #Stack
Solution
Simplify Path
#String #Stack
Solution
Next Largest Element to Right
#Stack
Solution
Next Largest Element to Left
#Stack
Solution
Next Smallest Element to Left
#Stack
Solution
Next Smallest Element to Right
#Stack
Solution
Maximum Area Rectangle in Binary Matrix
#Stack
Solution
Minimum Element in Stack with Extra Space
#Stack
Solution
Minimum Element in Stack in O
#Stack
Solution
Implement Queue using Stack
#Stack
Solution
Implement Stack using Queues
#Stack
Solution
Remove All Occurrences of a Substring
#Stack
Solution
Minimum invalid parentheses to remove to make valid parentheses
#Stack
Solution
Minimum Add to Make Parentheses Valid
#Stack
Solution
🧠

Mastering the Stack Pattern for Coding Interviews

1. Mental Model & Visual Intuition

The 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 Stack, the target time complexity is typically O(n) — each element pushed/popped at most 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.
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