Heap / Priority Queue Examples: Solved Problems with Step-by-Step Explanations
Learn the Heap / Priority Queue pattern through carefully selected examples. Each example includes problem statement, intuition, code in multiple languages, complexity analysis, and follow-up variations. Efficiently extract min/max elements using heap data structure.
Example 1: Classic Problem (Easy)
Problem 1: Easy difficulty Heap / Priority Queue problem
Approach: Apply the Heap / Priority Queue template with straightforward implementation.
Complexity: Time O(n log k), Space O(k)
Key Insight: Focus on pattern recognition and clean implementation.
Example 2: Interview Favorite (Medium)
Problem 2: Medium difficulty Heap / Priority Queue problem
Approach: Apply the Heap / Priority Queue template with optimized approach combining techniques.
Complexity: Time O(n log k), Space O(k)
Key Insight: Combine the base pattern with hash maps or sorting for optimization.
Example 3: Advanced Application (Hard)
Problem 3: Hard difficulty Heap / Priority Queue problem
Approach: Apply the Heap / Priority Queue template with advanced optimization and edge case handling.
Complexity: Time O(n log k), Space O(k)
Key Insight: Handle edge cases and optimize for large inputs.
Why These Examples Work
Each example was chosen because it:
Study the pattern, not just the solution. The goal is to recognize when to apply Heap / Priority Queue in new, unseen problems.
Pattern Recognition Checklist
Before coding, ask yourself:
If 2+ answers are yes, try the Heap / Priority Queue approach.
Categorization & Filters
Frequently Asked Questions
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