Distinct Subsequences

Understanding Distinct Subsequences: A Key Problem in String Manipulation

String manipulation is a cornerstone of computer science, often surfacing in real-world applications such as text processing and data analysis.

One such intriguing problem is finding Distinct Subsequences, which tests your ability to handle dynamic programming and combinatorics.

The Problem Statement

Given two strings s and t, determine how many distinct subsequences of s are equal to t.

  • What’s a Subsequence?
    A subsequence is a new string formed from the original string by deleting some or no characters without changing the relative order of the remaining characters. For example, “cat” is a subsequence of “caaat”.

Example Cases

  1. Input:
    s = "caaat", t = "cat"
    Output: 3
    Here, “cat” can be formed in three different ways from s.

  2. Input:
    s = "xxyxy", t = "xy"
    Output: 5
    There are five ways to form “xy” from s.

Constraints

  • The lengths of s and t range from 1 to 1000.
  • Both s and t consist of lowercase English letters.

Why is This Problem Important?

This problem challenges you to identify overlapping subproblems and use efficient techniques like dynamic programming to solve them. It highlights how subtle variations in character sequences can lead to vastly different outcomes.

Whether you’re preparing for coding interviews or honing your algorithmic skills, tackling the distinct subsequences problem is a great way to deepen your understanding of string manipulation.

Explanation:

Explanation: There are 3 different ways to sum the input numbers to get a sum of 2.
+2 +2 -2 = 2
+2 -2 +2 = 2
-2 +2 +2 = 2

Constraints:

  • 1 <= nums.length <= 20
  • 0 <= nums[i] <= 1000
  • -1000 <= target <= 1000

Approaching the Solution

To solve the interleaving string problem, one might consider using a dynamic programming or recursive approach to evaluate all possible ways to combine the strings. 

There are mainly Four approach to solve this problem – 

  1. Recursion 
  2. Dynamic Programming (Top-Down)
  3. Dynamic Programming (Bottom-up)
  4. Dynamic Programming (Space Optimized)

1.Recursion

  • Time complexity: O(2^m)
  • Space complexity: O(m)

2. Dynamic Programming (Top-Down)

Time & Space Complexity
  • Time complexity: O(m∗n)
  • Space complexity: O(m∗n)

3. Dynamic Programming (Bottom-Up)

Time & Space Complexity
  • Time complexity: O(m∗n)
  • Space complexity: O(m∗n)

4. Dynamic Programming (Space Optimized)

  • Time complexity: O(m∗n)
  • Space complexity: O(n)

Conclusion

The Interleaving String problem is more than just a test of string manipulation skills; it’s a practical exercise in algorithmic thinking. It demonstrates the power of logical sequencing and helps build a strong foundation for solving real-world problems where multiple sequences or inputs need to be merged in specific ways.

By tackling problems like this, you sharpen your ability to think critically and design efficient solutions for complex challenges.

More Articles