← Bank of America Interview Insights
The greedy insight is pretty straightforward once you see it: sort letters by frequency descending, then assign the highest beauty values to the most frequent ones.
First, clarify the problem: we need to assign values 1-26 to letters to maximize the sum of value × frequency. The optimal strategy is to assign the highest value (26) to the most frequent letter, the next highest (25) to the second most frequent, and so on. So, count the frequency of each letter (case-insensitive, ignoring non-letters), sort frequencies in descending order, and compute the weighted sum.
Pro tip: In a data science interview, emphasize that this is a greedy algorithm that is provably optimal by the rearrangement inequality. Also, mention that you would validate with edge cases like empty strings or strings with only punctuation.
Confirm that letters are case-insensitive, punctuation and spaces are ignored, and values 1-26 are assigned to letters a-z. Ask about input size and character set to ensure efficiency.
Iterate through the string, convert each character to lowercase, and if it's a letter, increment its count in a frequency array or hash map. Ignore non-letter characters.
Extract the frequency counts and sort them from highest to lowest. This ensures the most frequent letters get the highest values.
Starting with value 26, multiply each frequency by the current value, sum the products, and decrement the value for each subsequent frequency. Return the total sum.
State that time complexity is O(n + k log k) where n is string length and k is number of distinct letters (≤26), so effectively O(n). Test with empty string, all same letter, all distinct letters, and mixed case/punctuation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.