Leetcode #2207: Maximize Number of Subsequences in a String

In this guide, we solve Leetcode #2207 Maximize Number of Subsequences in a String in Python and focus on the core idea that makes the solution efficient.

You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.

Leetcode

Problem Statement

You are given a 0-indexed string text and another 0-indexed string pattern of length 2, both of which consist of only lowercase English letters. You can add either pattern[0] or pattern[1] anywhere in text exactly once.

Quick Facts

Difficulty: Medium
Premium: No
Tags: Greedy, String, Prefix Sum

Intuition

A locally optimal choice leads to a globally optimal result for this structure.

That means we can commit to decisions as we scan without backtracking.

Approach

Sort or preprocess if needed, then repeatedly take the best available local choice.

Maintain the minimal state necessary to validate the greedy decision.

Steps:

Sort or preprocess as needed.
Iterate and pick the best local option.
Track the current solution.

Example

Input: text = "abdcdbc", pattern = "ac"
Output: 4
Explanation:
If we add pattern[0] = 'a' in between text[1] and text[2], we get "abadcdbc". Now, the number of times "ac" occurs as a subsequence is 4.
Some other strings which have 4 subsequences "ac" after adding a character to text are "aabdcdbc" and "abdacdbc".
However, strings such as "abdcadbc", "abdccdbc", and "abdcdbcc", although obtainable, have only 3 subsequences "ac" and are thus suboptimal.
It can be shown that it is not possible to get more than 4 subsequences "ac" by adding only one character.

Python Solution

class Solution:
    def maximumSubsequenceCount(self, text: str, pattern: str) -> int:
        ans = x = y = 0
        for c in text:
            if c == pattern[1]:
                y += 1
                ans += x
            if c == pattern[0]:
                x += 1
        ans += max(x, y)
        return ans

Complexity

The time complexity is $O(n)$ , where $n$ is the length of the string $\textit{text}$ . The space complexity is $O(1)$ .

Edge Cases and Pitfalls

Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.

Summary

This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.

Example

Input: text = "abdcdbc", pattern = "ac"
Output: 4
Explanation:
If we add pattern[0] = 'a' in between text[1] and text[2], we get "abadcdbc". Now, the number of times "ac" occurs as a subsequence is 4.
Some other strings which have 4 subsequences "ac" after adding a character to text are "aabdcdbc" and "abdacdbc".
However, strings such as "abdcadbc", "abdccdbc", and "abdcdbcc", although obtainable, have only 3 subsequences "ac" and are thus suboptimal.
It can be shown that it is not possible to get more than 4 subsequences "ac" by adding only one character.

Python Solution

class Solution:
    def maximumSubsequenceCount(self, text: str, pattern: str) -> int:
        ans = x = y = 0
        for c in text:
            if c == pattern[1]:
                y += 1
                ans += x
            if c == pattern[0]:
                x += 1
        ans += max(x, y)
        return ans

Leetcode #2207: Maximize Number of Subsequences in a String

Problem Statement

Quick Facts

Intuition

Approach

Example

Python Solution

Complexity

Edge Cases and Pitfalls

Summary

Ace your next coding interview

Leetcode #2207: Maximize Number of Subsequences in a String

Problem Statement

Quick Facts

Intuition

Approach

Example

Python Solution

Complexity

Edge Cases and Pitfalls

Summary

Ace your next coding interview