Leetcode Substring With Concatenation Of All Words
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Leetcode Substring With Concatenation Of All Words

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LeetCode is a widely recognized platform for coding practice and algorithm challenges that helps programmers of all levels improve their problem-solving skills. One particularly challenging problem is Substring with Concatenation of All Words, which tests understanding of string manipulation, hashing, and sliding window techniques. This problem requires identifying all starting indices in a string where a concatenation of a given list of words occurs exactly once without any intervening characters. Solving this problem efficiently involves careful consideration of time complexity, data structures, and algorithmic patterns, making it an excellent exercise for coding interviews and competitive programming.

Understanding the Problem

The Substring with Concatenation of All Words problem can be described as follows given a stringsand a list of wordswordsof equal length, find all starting indices inswhere a concatenation of each word inwordsexactly once appears consecutively. The order of words does not matter, but there should be no intervening characters. This problem combines elements of substring search, hash maps, and sliding windows to create an efficient solution.

Key Concepts

  • Each word in thewordslist has the same length.
  • The total length of the concatenated substring is the product of the word length and the number of words.
  • Duplicate words in thewordslist need to be counted correctly.
  • Order of words is flexible, but every word must appear exactly once in the substring.

Approach to Solve the Problem

There are multiple approaches to solve this LeetCode problem, ranging from brute force to optimized techniques. Understanding the trade-offs between simplicity and efficiency is crucial. A naive approach might involve generating all permutations of the words and checking each substring of the string, but this quickly becomes computationally infeasible as the number of words increases. Efficient approaches typically involve hash maps and sliding window strategies.

Brute Force Approach

The brute force method involves the following steps

  • Generate all possible concatenations of the words in the list.
  • Iterate through the stringsand check if any substring matches one of these concatenations.
  • Record the starting index of any matches found.

While conceptually simple, this approach is inefficient because the number of permutations grows factorially with the number of words. As a result, it is unsuitable for larger inputs.

Optimized Approach Using Sliding Window and Hash Map

A more efficient solution uses a sliding window technique combined with hash maps to track word counts

  • Compute the length of each word and the total length of the concatenated substring.
  • Create a hash map (wordCount) to store the frequency of each word in the list.
  • Iterate over the string with a window of the total length.
  • Within the window, extract substrings of word length and store their counts in another hash map (seenWords).
  • CompareseenWordswithwordCount; if they match, the starting index is valid.
  • Move the window forward and repeat the process for each possible offset up to the word length.

Step-by-Step Implementation

To implement the optimized solution in code, the following steps are typically followed

Step 1 Initialize Variables

  • Calculate the length of each word usingwordLength = words[0].length().
  • Calculate total concatenated lengthsubstringLength = wordLength words.length.
  • Create awordCounthash map to count occurrences of each word inwords.

Step 2 Iterate Through the String

Loop through each possible offset in0..wordLength-1and apply a sliding window

  • Start a left pointer at the offset.
  • Iterate through the string in increments of word length.
  • Extract the current word and check if it exists inwordCount.
  • If it exists, increment its count inseenWords; if not, resetseenWordsand move the left pointer.
  • IfseenWordsexceeds the expected frequency, shrink the window from the left.
  • WhenseenWordsmatcheswordCount, record the starting index.

Step 3 Return the Result

After iterating through all offsets and sliding windows, collect all valid starting indices into a list and return it as the result.

Example

Consider the strings = barfoothefoobarman"and wordswords = ["foo","bar"]. The concatenated substring length is 6. Sliding through the string

  • Check the substring starting at index 0 “barfoo” → valid, record index 0.
  • Check the substring starting at index 3 “foothe” → invalid.
  • Check the substring starting at index 9 “foobar” → valid, record index 9.
  • Return[0, 9]as the final answer.

Time Complexity Analysis

The time complexity of the optimized approach is O(n wordLength), where n is the length of the string. This is because each character is processed at most once per offset, and the number of offsets is limited to the word length. Using hash maps ensures that word frequency comparison is efficient, typically O(1) per word.

Space Complexity

Space complexity is O(k), where k is the number of unique words. Hash maps are used to store the frequency of each word in both the original list and the current sliding window. This makes the approach memory-efficient even for longer strings with many words.

Common Pitfalls

  • Failing to handle duplicate words correctly the program must track the exact frequency of each word.
  • Not iterating through all possible offsets missing valid substrings that start at positions not aligned with the first character of a word.
  • Incorrectly calculating substring length leading to windows that are too short or too long.
  • Ignoring edge cases such as empty string or empty word list.

Applications and Benefits

Solving this LeetCode problem provides multiple benefits for programmers

  • Strengthens skills in string manipulation and substring analysis.
  • Teaches efficient use of hash maps and sliding window techniques.
  • Prepares for coding interviews where similar pattern matching problems are common.
  • Develops logical thinking and problem decomposition skills.
  • Introduces optimization techniques that reduce time and space complexity.

The Substring with Concatenation of All Words problem on LeetCode is a challenging yet rewarding exercise for programmers looking to improve their understanding of strings, hash maps, and sliding window algorithms. By analyzing the problem carefully, applying an optimized approach using hash maps, and considering edge cases, it is possible to create an efficient solution that scales for larger inputs. Practicing this problem helps build the foundation for solving more complex substring and pattern matching problems in competitive programming and real-world applications.

Overall, mastering this problem enhances algorithmic thinking, improves coding efficiency, and prepares developers for similar challenges in technical interviews. With consistent practice, understanding of hash maps, sliding windows, and string manipulation becomes intuitive, enabling programmers to tackle a wide variety of string-related problems confidently.