top of page
Heaps & Greedy Algorithms
12 problems · Easy to Medium · Heaps and greedy strategies
Greedy algorithms and heaps share a common theme: making locally optimal choices to achieve a globally optimal result. This track covers interval scheduling, jump game variants, and Kadane's algorithm for maximum subarray before moving into heap-based problems like K Closest Points, Median from Data Stream, and the classic Task Scheduler with Cooling Interval.
Why it matters:
Greedy problems are deceptively tricky — the hard part isn't the code, it's proving to yourself and the interviewer that the greedy choice is always safe. Interviewers use these problems to test whether you can articulate the reasoning behind an approach, not just implement it. A confident explanation of why greedy works is often more impressive than the solution itself.
bottom of page