How do you solve Eat Pizzas! on LeetCode?

Eat Pizzas! (LeetCode #3457) 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: Always sort the array to easily access the largest weights.

What is the time complexity of Eat Pizzas!?

The optimal solution for Eat Pizzas! runs in O(n log n) time and uses O(1) space. The sorting step dominates the complexity, making it O(n log n), while the subsequent loop runs in linear time.

What is the brute force solution for Eat Pizzas!?

The brute force approach for Eat Pizzas! is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we will try all possible combinations of pizzas for each day and calculate the total weight gained. This is straightforward but inefficient as it checks every possible grouping. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Eat Pizzas!?

Eat Pizzas! uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting.

What are the interview tips for Eat Pizzas!?

Explain your thought process clearly as you work through the problem. Consider edge cases and constraints before finalizing your approach. Practice similar problems to become familiar with patterns and strategies.

What are common mistakes when solving Eat Pizzas!?

Not sorting the pizzas before making selections. Failing to account for the day (odd/even) when determining weight gain.

#3457

Eat Pizzas!

Medium

ArrayGreedySortingGreedySorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

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

In the optimal solution, we sort the pizzas and strategically select the largest weights based on the day. This allows us to maximize the weight gained efficiently.

⚙️

Algorithm

3 steps

1Step 1: Sort the pizzas in ascending order.
2Step 2: Initialize total weight gained to 0.
3Step 3: Iterate through the pizzas, taking the largest pizza for odd days and the second largest for even days, while grouping them optimally.

solution.py13 lines

1# Full working Python code
2
3def maxWeight(pizzas):
4    pizzas.sort()
5    total_weight = 0
6    n = len(pizzas)
7    for day in range(n // 4):
8        if day % 2 == 0:
9            total_weight += pizzas[n - 1 - (day * 4)]  # Z
10        else:
11            total_weight += pizzas[n - 2 - (day * 4)]  # Y
12    return total_weight
13

ℹ

Complexity note: The sorting step dominates the complexity, making it O(n log n), while the subsequent loop runs in linear time.

1Always sort the array to easily access the largest weights.
2Group pizzas optimally based on the day to maximize weight gain.

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