Interview Questions : MAANG (FAANG) : Longest Substring Without Repeating Characters
Question Link : Longest Substring Without Repeating Characters
In the realm of coding interviews and algorithmic challenges, one often encounters problems that test the ability to manage and manipulate strings efficiently. A common yet intriguing problem is finding the length of the longest substring without repeating characters. This problem not only tests your understanding of strings but also your ability to apply sliding window techniques and hashing for efficient computation. Let’s delve into my thought process and approach to solving this problem, which I’ve also covered in my YouTube channel dedicated to data structures and algorithms in both English and Hindi. Whether you’re preparing for interviews or just looking to sharpen your coding skills, these resources can be invaluable.
Intuition
Upon first glance, the problem seems straightforward but quickly reveals its complexity when considering the need to track repeating characters and calculate lengths dynamically. My initial thought was to use a method that allows me to iterate through the string while keeping track of characters I’ve already seen and their positions. This way, whenever a repeat character is encountered, I can adjust the starting point of the substring being considered without having to re-scan previously evaluated parts of the string.
Approach
The strategy I decided on involves using a sliding window technique combined with a hash map (or array in this case, since the input is limited to ASCII characters) to keep track of the last index at which each character appears. The steps are as follows:
1. Initialize a hash map (or array in this case, `dict`) with a size sufficient to cover all ASCII characters, setting all values initially to `-1` to indicate that no characters have been encountered yet.
2. Set `maxLen` to 0 to keep track of the maximum length found and `start` to -1 to mark the beginning of the current substring.
3. Iterate through each character in the string. For each character:
— If the current character has been seen before (i.e., `dict[s[i]] > start`), update `start` to the last seen position of that character. This effectively shrinks the sliding window to exclude the repeating character and everything before it.
— Update the position of the current character in `dict`.
— Calculate the length of the current substring as `i — start` and update `maxLen` if this new length is greater than the current `maxLen`.
4. Return `maxLen` as the length of the longest substring without repeating characters.
Complexity
- Time complexity:O(n), where n is the length of the string. Each character in the string is visited at most twice, once by the iterator and once by the `start` pointer.
- Space complexity:O(1), despite using an auxiliary data structure (`dict`), the size is constant (256) and does not scale with the size of the input string.
This solution provides an efficient and elegant way to solve the problem, leveraging the strengths of hash maps to keep track of character positions and the sliding window technique to dynamically adjust the considered substring.
For those interested in further exploring such problems and solutions, I invite you to check out my YouTube channel. Whether you prefer content in English or Hindi, you’ll find a wealth of resources on interview questions, data structures, and algorithms to help you on your coding journey:
Interview questions, Data Structure, and algorithm in English:(https://www.youtube.com/playlist?list=PLmC7cmdfHc2apf_s-Pl8UUJ74xa_bP759)
Interview questions, Data Structure, and algorithm in Hindi (https://www.youtube.com/playlist?list=PLmC7cmdfHc2bCtYQiQGWDjWQR4rg2zKSA)
I hope this solution and explanation help you grasp the concept and approach to solving such string manipulation problems efficiently. Happy coding!
