What is the time complexity of Minimum Deletions for At Most K Distinct Characters?

The optimal solution for Minimum Deletions for At Most K Distinct Characters runs in O(n log n) time and uses O(n) space. Linear for counting, log n for sorting.

What is the brute force solution for Minimum Deletions for At Most K Distinct Characters?

The brute force approach for Minimum Deletions for At Most K Distinct Characters is Brute Force, with O(n²) time complexity and O(1) space complexity. Generate all possible substrings and count distinct characters. This is inefficient but straightforward. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Minimum Deletions for At Most K Distinct Characters?

Minimum Deletions for At Most K Distinct Characters uses the following concepts: Hash Table, String, Greedy, Sorting, Counting. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Minimum Deletions for At Most K Distinct Characters?

Explain your thought process clearly. Consider edge cases upfront. Practice coding without an IDE.

What are common mistakes when solving Minimum Deletions for At Most K Distinct Characters?

Not counting frequencies correctly. Forgetting to handle cases where k >= distinct characters.

#3545

Minimum Deletions for At Most K Distinct Characters

Easy

Hash TableStringGreedySortingCountingHash MapArray

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Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

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

Count character frequencies, sort them, and remove the least frequent characters until we have at most k distinct characters.

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Algorithm

3 steps

1Step 1: Count the frequency of each character in s.
2Step 2: Sort the frequencies in ascending order.
3Step 3: Remove characters from the start of the sorted list until only k distinct characters remain, summing their frequencies.

solution.py5 lines

1def min_deletions_optimal(s, k):
2    from collections import Counter
3    freq = Counter(s)
4    counts = sorted(freq.values())
5    return sum(counts[:-k]) if len(counts) > k else 0

ℹ

Complexity note: Linear for counting, log n for sorting.

1Character frequency matters.
2Sorting helps in prioritizing deletions.

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