How do you solve Array Reduce Transformation on LeetCode?

Array Reduce Transformation (LeetCode #2626) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Understanding how to apply a function iteratively is crucial in functional programming.

What is the brute force solution for Array Reduce Transformation?

The brute force approach for Array Reduce Transformation is Brute Force, with O(n) time complexity and O(1) space complexity. The brute-force approach involves iterating through the array and applying the reducer function sequentially. This is straightforward and easy to understand, but not the most efficient way to handle the problem. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Array Reduce Transformation?

Always clarify the behavior of the function with edge cases, such as empty arrays. Explain your thought process while coding; it helps the interviewer understand your approach. Practice writing clean and efficient code without relying on built-in functions.

What are common mistakes when solving Array Reduce Transformation?

Forgetting to handle the case of an empty array, which should return the initial value. Not correctly applying the reducer function, leading to incorrect results.

#2626

Array Reduce Transformation

Easy

Functional ProgrammingArray Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(1)

The optimal solution is essentially the same as the brute force approach, but we clarify that it runs in linear time and handles edge cases efficiently. This approach is direct and leverages the reducer function effectively.

⚙️

Algorithm

4 steps

1Step 1: Initialize a variable 'res' to the initial value 'init'.
2Step 2: Loop through each element in the array 'nums'.
3Step 3: For each element, update 'res' by calling the reducer function 'fn' with 'res' and the current element.
4Step 4: Return 'res' after processing all elements.

solution.py5 lines

1def arrayReduce(nums, fn, init):
2    res = init
3    for num in nums:
4        res = fn(res, num)
5    return res

ℹ

Complexity note: The time complexity remains O(n) as we still iterate through the array once. The space complexity is O(1) because we only use a single variable to store the result.

1Understanding how to apply a function iteratively is crucial in functional programming.
2Recognizing that the reducer function can be any binary operation allows for flexibility in problem-solving.

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