How do you solve Sort By on LeetCode?

Sort By (LeetCode #2724) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Sorting can be customized using a function to determine order.

What is the time complexity of Sort By?

The optimal solution for Sort By runs in O(n log n) time and uses O(1) space. The time complexity remains O(n log n) due to sorting, but we optimize space usage by sorting in place when possible.

What is the brute force solution for Sort By?

The brute force approach for Sort By is Brute Force, with O(n log n) time complexity and O(n) space complexity. The brute force approach involves sorting the array by applying the function to each element and then sorting based on the results. This is straightforward but inefficient for larger arrays. The optimal Optimal Solution approach improves this to O(n log n).

What are the interview tips for Sort By?

Always clarify the behavior of the function fn before starting to implement. Consider edge cases, such as empty arrays or arrays with one element. Practice explaining your thought process as you code to simulate the interview environment.

What are common mistakes when solving Sort By?

Overcomplicating the sorting logic instead of using built-in functions. Not considering the implications of space complexity when creating new arrays.

#2724

Sort By

Easy

SortingCustom Comparators

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n log n)Space O(1)

The optimal solution leverages built-in sorting functions that can directly sort the array based on the output of the function fn, making it both concise and efficient.

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Algorithm

2 steps

1Step 1: Use the built-in sort function on the array.
2Step 2: Pass a custom comparator that applies fn to determine the order of sorting.

solution.py2 lines

1def sortBy(arr, fn):
2    return sorted(arr, key=fn)

ℹ

Complexity note: The time complexity remains O(n log n) due to sorting, but we optimize space usage by sorting in place when possible.

1Sorting can be customized using a function to determine order.
2Understanding built-in sorting mechanisms can lead to more efficient solutions.

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