How do you solve Maximum Tastiness of Candy Basket on LeetCode?

Maximum Tastiness of Candy Basket (LeetCode #2517) 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 the prices allows for efficient selection of candies based on their price differences.

What is the brute force solution for Maximum Tastiness of Candy Basket?

The brute force approach for Maximum Tastiness of Candy Basket is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking all possible combinations of k candies from the price array. By calculating the minimum absolute difference of prices for each combination, we can find the maximum tastiness. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Tastiness of Candy Basket?

Maximum Tastiness of Candy Basket uses the following concepts: Array, Binary Search, Greedy, Sorting. The recommended patterns to study are: Binary Search, Greedy Algorithms, Sorting.

What are the interview tips for Maximum Tastiness of Candy Basket?

Always start with a brute force solution to understand the problem better. Explain your thought process clearly while transitioning to an optimal solution. Practice writing clean and efficient code, as readability is crucial in interviews.

What are common mistakes when solving Maximum Tastiness of Candy Basket?

Not considering edge cases where all prices are the same, leading to a tastiness of 0. Failing to optimize the search for combinations, leading to timeouts on larger inputs.

#2517

Maximum Tastiness of Candy Basket

Medium

ArrayBinary SearchGreedySortingBinary SearchGreedy AlgorithmsSorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

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

The optimal solution leverages sorting and binary search. By sorting the prices, we can efficiently check if we can select k candies with a minimum tastiness using a greedy approach.

⚙️

Algorithm

3 steps

1Step 1: Sort the price array.
2Step 2: Use binary search to determine the maximum possible tastiness.
3Step 3: For each mid value in the binary search, use a greedy approach to check if we can select k candies such that the minimum difference is at least mid.

solution.py21 lines

1def canSelectCandies(price, k, min_diff):
2    count = 1
3    last_selected = price[0]
4    for i in range(1, len(price)):
5        if price[i] - last_selected >= min_diff:
6            count += 1
7            last_selected = price[i]
8            if count == k:
9                return True
10    return False
11
12def maxTastiness(price, k):
13    price.sort()
14    left, right = 0, price[-1] - price[0]
15    while left < right:
16        mid = (left + right + 1) // 2
17        if canSelectCandies(price, k, mid):
18            left = mid
19        else:
20            right = mid - 1
21    return left

ℹ

Complexity note: The sorting step takes O(n log n) time, and the binary search runs in O(log(max_diff)) where max_diff is the range of possible tastiness values. The greedy check runs in O(n), leading to an overall efficient solution.

1Sorting the prices allows for efficient selection of candies based on their price differences.
2Binary search helps in narrowing down the maximum tastiness efficiently.

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