Request senior-level coding problems focused on algorithms and complexity
Request senior-level coding problems focused on algorithms and complexity
CODING
1. Merge K Sorted Linked Lists
Problem: Given k sorted linked lists, merge them into one sorted linked list.
Key Algorithm: Use a min-heap (priority queue) to efficiently find the smallest head among k lists.
Time Complexity: O(Nlogk)O(N \log k)O(Nlogk) where NNN is total number of nodes, kkk is number of lists.
2. Trapping Rain Water
Problem: Given an array of elevations, compute how much water it can trap after raining.
Key Algorithm: Two-pointer approach maintaining left_max and right_max to compute trapped water in O(N).
Time Complexity: O(N)O(N)O(N), Space Complexity: O(1)O(1)O(1).
3. Word Break (DP)
Problem: Given a string and a dictionary, determine if the string can be segmented into valid words.
Key Algorithm: Use dynamic programming with a boolean DP array indicating if substrings can be segmented.
Time Complexity: O(N2)O(N^2)O(N2), with NNN as length of string.
4. LRU Cache Implementation
Problem: Design an LRU cache with O(1) get and put operations.
Key Data Structures: Use OrderedDict or combine doubly linked list and hashmap for O(1) operations.
Time Complexity: O(1) for both get and put.
5. Median in Data Stream
Problem: Find the median at any point as numbers are streamed.
Key Algorithm: Use two heaps (max-heap and min-heap) to balance lower and upper halves.
Time Complexity: O(log N) per insertion.
6. Number of Islands (Graph DFS)
Problem: Count connected components of '1's in 2D grid.
Key Algorithm: DFS or BFS traversal of grid to mark visited cells.
Time Complexity: O(M×N)O(M \times N)O(M×N), where MMM and NNN are grid dimensions.
Complexity Analysis and Approach Strategy
Always start with a brute force solution: analyze its time and space complexity (often O(N2)O(N^2)O(N2) or worse).
Brainstorm optimizations: introducing data structures like heaps, tries, balanced trees, or using DP/memoization.
Consider input constraints to choose between solutions (e.g., N≤105N \leq 10^5N≤105 favors O(NlogN)O(N \log N)O(NlogN) or better).
Use known algorithmic patterns: sliding windows (O(N)), divide and conquer (O(N log N)), two pointers (O(N)).
Articulate space-time tradeoffs clearly during interviews.
Recommended Practice
Work on these problems on platforms like LeetCode, HackerRank, or InterviewBit.
After solving, write down time and space complexity, and try alternate approaches for optimization.
Engage in mock interviews focusing on explaining your complexity reasoning.
This approach builds both problem-solving skills and a deep understanding of complexity analysis valued at senior levels.