Continuing with the 75 Blind Leetcode Questions, the problems tackled in this second week are more advanced, diving deep into linked lists, arrays, dynamic programming, and graph traversal techniques. These challenges are common in technical interviews and are great for honing skills in optimal problem-solving. Below, I analyze each problem, focusing on solution steps, time and space complexity, and key conclusions.
1. Reverse a Linked List
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(1)
- Conclusion: This problem showcases an efficient in-place solution with a linear runtime. The iterative approach is optimal, though a recursive solution is possible but may incur extra space overhead.
2. Detect Cycle in a Linked List
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(1)
- Conclusion: The two-pointer (Floyd's cycle detection algorithm) is the most efficient approach for detecting cycles in a linked list, using constant space and linear time.
3. Container With Most Water
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(1)
- Conclusion: This problem demonstrates an optimal O(n) solution using two pointers, significantly better than the O(n²) brute force solution. It’s a classic problem that tests the understanding of how to use multiple pointers to optimize space and time.
4. Find Minimum in Rotated Sorted Array
- Solution Steps:
- Time Complexity: O(log n)
- Space Complexity: O(1)
- Conclusion: This problem uses binary search, a fundamental algorithm in computer science. The key is to recognize the sorted portions of the rotated array, making this a classic interview question.
5. Longest Repeating Character Replacement
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(1) if we assume only 26 characters (constant space).
- Conclusion: This problem is a great application of sliding window techniques, which are often used in optimization problems. It allows for efficient handling of the problem with linear time complexity.
6. Longest Substring Without Repeating Characters
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(min(n, m)), where n is the size of the string and m is the size of the character set.
- Conclusion: Sliding window with a hash set allows for efficient detection of substrings without duplicates. This problem is a common interview favorite due to its focus on string manipulation and optimization.
7. Number of Islands
- Solution Steps:
- Time Complexity: O(m * n), where m is the number of rows and n is the number of columns.
- Space Complexity: O(m * n) for the recursive stack or BFS queue.
- Conclusion: This is a classic graph traversal problem disguised as a grid. Both DFS and BFS can be used, making it a versatile example of how different traversal methods can solve similar problems.
8. Remove Nth Node From End of List
- Solution Steps:
- Time Complexity: O(n)
- Space Complexity: O(1)
- Conclusion: The two-pointer technique efficiently handles this problem in a single pass through the list. It’s a frequent interview question and a great exercise in linked list manipulation.
9. Palindromic Substrings
- Solution Steps:
- Time Complexity: O(n²)
- Space Complexity: O(1)
- Conclusion: Though the brute-force approach is O(n³), expanding around the center reduces the time complexity to O(n²), which is sufficient for most interview cases.
10. Pacific Atlantic Water Flow
- Solution Steps:
- Time Complexity: O(m * n)
- Space Complexity: O(m * n)
- Conclusion: This problem is a fantastic example of using graph traversal (DFS/BFS) in grid problems. It’s challenging yet demonstrates key traversal and marking techniques in competitive programming.
11. Minimum Window Substring
- Solution Steps:
- Time Complexity: O(n + m), where n is the length of the string s and m is the length of t.
- Space Complexity: O(m) for storing the character counts.
- Conclusion: This is a classic sliding window problem, often asked in interviews due to its complexity in managing the window size while ensuring all conditions are met. It tests optimization skills and string manipulation.
Overall Conclusion:
These problems represent a combination of graph traversal, dynamic programming, sliding window techniques, and linked list manipulation, all crucial for technical interviews. Mastering these techniques not only helps in optimizing time and space complexities but also builds a deep understanding of algorithmic patterns. Each problem requires a thoughtful approach to choose the right data structure and algorithm, whether it’s using two-pointer techniques, DFS/BFS, or binary search. Completing this
