What is the time complexity of Count K-Subsequences of a String With Maximum Beauty?

The optimal solution for Count K-Subsequences of a String With Maximum Beauty runs in O(n log n) time and uses O(n) space. The complexity is O(n log n) due to sorting the frequency counts, while space complexity is O(n) for storing the frequency counts.

What is the brute force solution for Count K-Subsequences of a String With Maximum Beauty?

The brute force approach for Count K-Subsequences of a String With Maximum Beauty is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves generating all possible k-subsequences from the string and calculating their beauty. This method is straightforward but inefficient, as it checks every combination. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Count K-Subsequences of a String With Maximum Beauty?

Count K-Subsequences of a String With Maximum Beauty uses the following concepts: Hash Table, Math, String, Greedy, Sorting, Combinatorics. The recommended patterns to study are: Hash Map, Sorting.

What are the interview tips for Count K-Subsequences of a String With Maximum Beauty?

Always clarify constraints and edge cases before diving into coding. Think about the efficiency of your solution; brute force is rarely optimal. Practice explaining your thought process clearly as you code.

What are common mistakes when solving Count K-Subsequences of a String With Maximum Beauty?

Not checking if k exceeds the number of unique characters. Confusing subsequences with combinations.

#2842

Count K-Subsequences of a String With Maximum Beauty

Hard

Hash TableMathStringGreedySortingCombinatoricsHash MapSorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n log n)
Space	O(1)	O(n)

💡

Intuition

Time O(n log n)Space O(n)

The optimal solution focuses on counting the frequencies of characters, sorting them, and selecting the top k frequencies to maximize beauty. This avoids generating combinations and directly computes the result.

⚙️

Algorithm

5 steps

1Step 1: Count the frequency of each character in the string.
2Step 2: If k is greater than the number of unique characters, return 0.
3Step 3: Sort the frequencies in descending order.
4Step 4: Sum the top k frequencies to get the maximum beauty.
5Step 5: Count how many ways we can choose these characters to achieve the maximum beauty.

solution.py14 lines

1# Full working Python code
2from collections import Counter
3import math
4
5def countKSubsequences(s, k):
6    freq = Counter(s)
7    if k > len(freq): return 0
8    sorted_freq = sorted(freq.values(), reverse=True)
9    max_beauty = sum(sorted_freq[:k])
10    count = 1
11    for i in range(k):
12        count *= freq[sorted_freq[i]]
13        count %= (10**9 + 7)
14    return count

ℹ

Complexity note: The complexity is O(n log n) due to sorting the frequency counts, while space complexity is O(n) for storing the frequency counts.

1Understanding the frequency of characters is crucial for maximizing beauty.
2Sorting helps in easily accessing the top k frequencies.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.