15 ‘Hard’ leetcode problems that are actually easy

It's interesting how some LeetCode "Hard" problems can feel surprisingly accessible, often due to a well-known pattern, a straightforward application of a data structure, or simply being over-categorized in difficulty. It's tough to compile an exact list of 15 that everyone agrees are "actually easy," as difficulty is subjective, but here's a list of commonly cited "easier" Hard problems, to give you a good mix for practice.

Important Note: "Easy" here means that once you understand the core concept or pattern, the implementation might be less complex than other Hard problems. These still require solid problem-solving skills!

Here are some LeetCode "Hard" problems often considered relatively easier :

"Easier" Hard Problems (approx. 15-20):

1. Median of Two Sorted Arrays (Hard)

   • Link: https://leetcode.com/problems/median-of-two-sorted-arrays/
   • Why: While the optimal $O(\log(\min(m,n)))$ solution uses binary search and can be tricky, the problem itself is conceptually clear. Many find the approach of finding the k-th element in two sorted arrays to be a well-defined pattern once learned.

2. Trapping Rain Water (Hard)

   • Link: https://leetcode.com/problems/trapping-rain-water/
   • Why: Often solvable with a two-pointer approach or by pre-calculating left/right max heights. The intuition is quite strong once you visualize it.

3. Sliding Window Maximum (Hard)

   • Link: https://leetcode.com/problems/sliding-window-maximum/
   • Why: Solvable with a deque (double-ended queue). It's a classic pattern for finding maximum/minimum in a sliding window, and once you grasp the deque logic, it becomes more manageable.

4. Merge k Sorted Lists (Hard)

   • Link: https://leetcode.com/problems/merge-k-sorted-lists/
   • Why: Can be solved efficiently using a min-heap, which is a fairly standard data structure. The merge sort-like divide and conquer approach also works.

5. Longest Consecutive Sequence (Hard)

   • Link: https://leetcode.com/problems/longest-consecutive-sequence/
   • Why: Often solved using a hash set. The key is to optimize by only starting a sequence check if the current number is the start of a new sequence (i.e., its predecessor is not in the set).

6. Largest Rectangle in Histogram (Hard)

   • Link: https://leetcode.com/problems/largest-rectangle-in-histogram/
   • Why: A classic monotonic stack problem. Once you understand the concept of a monotonic stack, this problem becomes a prime example.

7. Word Ladder (Hard)

   • Link: https://leetcode.com/problems/word-ladder/
   • Why: Essentially a BFS problem on a graph where words are nodes and an edge exists if they differ by one character. If you're comfortable with BFS, this can be quite approachable.

8. N-Queens (Hard)

   • Link: https://leetcode.com/problems/n-queens/
   • Why: A backtracking problem. While it requires careful state management, the core idea of placing queens and checking for conflicts is systematic.

9. Swim in Rising Water (Hard)

   • Link: https://leetcode.com/problems/swim-in-rising-water/
   • Why: Can be solved using Dijkstra's algorithm or a similar shortest path algorithm on a grid. If you know graph traversals, it's a direct application.

10. Find Median from Data Stream (Hard)

    • Link: https://leetcode.com/problems/find-median-from-data-stream/
    • Why: Solvable with two heaps (max-heap for the lower half, min-heap for the upper half). A common pattern for median tracking.

11. Making A Large Island (Hard)

    • Link: https://leetcode.com/problems/making-a-large-island/
    • Why: Involves DFS/BFS for island traversal and potentially a union-find-like approach to combine islands. If you're comfortable with graph traversal on grids, it's manageable.

12. Longest Increasing Path in a Matrix (Hard)

    • Link: https://leetcode.com/problems/longest-increasing-path-in-a-matrix/
    • Why: Often solved with DFS + memoization (dynamic programming). The key is to avoid cycles and use memoization to store results of subproblems.

13. Count of Smaller Numbers After Self (Hard)

    • Link: https://leetcode.com/problems/count-of-smaller-numbers-after-self/
    • Why: Can be solved with a Fenwick tree (BIT) or segment tree, or a modified merge sort. If you know these data structures, it's a direct application.

14. Minimum Window Substring (Hard)

    • Link: https://leetcode.com/problems/minimum-window-substring/
    • Why: A classic sliding window problem. Requires careful management of character counts and window expansion/contraction.

15. Burst Balloons (Hard)

    • Link: https://leetcode.com/problems/burst-balloons/
    • Why: This is a Dynamic Programming problem that can be tricky to conceptualize for the first time, but once you see the "interval DP" approach (where you iterate on the last burst balloon), it becomes a standard DP pattern.

16. Alien Dictionary (Hard - LeetCode Premium)

    • Link: (Premium) https://leetcode.com/problems/alien-dictionary/
    • Why: This is a classic topological sort problem. If you understand how to build a graph from word comparisons and then apply topological sort, it's straightforward.