You are given an array of positive integers price
where price[i]
denotes the price of the ith
candy and a positive integer k
.
The store sells baskets of k
distinct candies. The tastiness of a candy basket is the smallest absolute difference of the prices of any two candies in the basket.
Return the maximum tastiness of a candy basket.
Example 1:
Input: price = [13,5,1,8,21,2], k = 3 Output: 8 Explanation: Choose the candies with the prices [13,5,21]. The tastiness of the candy basket is: min(|13 - 5|, |13 - 21|, |5 - 21|) = min(8, 8, 16) = 8. It can be proven that 8 is the maximum tastiness that can be achieved.
Example 2:
Input: price = [1,3,1], k = 2 Output: 2 Explanation: Choose the candies with the prices [1,3]. The tastiness of the candy basket is: min(|1 - 3|) = min(2) = 2. It can be proven that 2 is the maximum tastiness that can be achieved.
Example 3:
Input: price = [7,7,7,7], k = 2 Output: 0 Explanation: Choosing any two distinct candies from the candies we have will result in a tastiness of 0.
Constraints:
2 <= k <= price.length <= 105
1 <= price[i] <= 109
When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:
The goal is to find the best possible candy basket by trying every single combination of candies. We'll consider all possible baskets and choose the one with the highest 'tastiness' score.
Here's how the algorithm would work step-by-step:
def maximum_tastiness_brute_force(candies):
number_of_candies = len(candies)
maximum_tastiness = 0
# Iterate through all possible subset sizes
for subset_size in range(1, number_of_candies + 1):
# Generate all combinations of candies of the current size
combinations = get_combinations(candies, subset_size)
# Calculate the tastiness of each combination and update the maximum
for combination in combinations:
current_tastiness = sum(combination)
maximum_tastiness = max(maximum_tastiness, current_tastiness)
return maximum_tastiness
def get_combinations(items, subset_size):
if subset_size == 0:
return [[]]
if not items:
return []
combinations = []
first_item = items[0]
rest_of_items = items[1:]
# Include the first item in the subset
combinations_with_first = get_combinations(rest_of_items, subset_size - 1)
for combination in combinations_with_first:
combinations.append([first_item] + combination)
# Exclude the first item from the subset
combinations_without_first = get_combinations(rest_of_items, subset_size)
combinations.extend(combinations_without_first)
return combinations
# Example Usage
# candies = [1, 2, 3, 4, 5]
# result = maximum_tastiness_brute_force(candies)
# print(f"Maximum Tastiness: {result}")
The problem asks to pick candies such that the smallest tastiness difference between any two chosen candies is as big as possible. The clever approach is to first sort the candies and then use a process to find the maximum possible minimum difference without testing all the combinations.
Here's how the algorithm would work step-by-step:
def maximum_tastiness(tastiness, k):
tastiness.sort()
left_value = 0
right_value = tastiness[-1] - tastiness[0]
max_tastiness = 0
while left_value <= right_value:
mid_value = (left_value + right_value) // 2
# Check if it's possible to pick candies with this min tastiness.
if can_pick(tastiness, k, mid_value):
# If possible, try a larger tastiness.
max_tastiness = mid_value
left_value = mid_value + 1
else:
# If not possible, try a smaller tastiness.
right_value = mid_value - 1
return max_tastiness
def can_pick(tastiness, k, min_tastiness):
count = 1
previous_candy = tastiness[0]
# Greedily pick candies.
for i in range(1, len(tastiness)):
if tastiness[i] - previous_candy >= min_tastiness:
# Only increment when candy is picked.
count += 1
previous_candy = tastiness[i]
if count == k:
return True
return False
Case | How to Handle |
---|---|
Empty candies array | Return 0 as no tastiness is possible with no candies. |
candies array with only one element | Return 0 as a basket needs at least two candies. |
All candies have the same tastiness value | The algorithm should still correctly identify the two candies with the maximum possible tastiness within the limit. |
Large number of candies, potentially exceeding memory limits. | The solution should be optimized for space complexity, possibly using a streaming or iterative approach if the entire candies array cannot fit in memory. |
tastiness values that cause integer overflow when summed | Use a data type with a larger range (e.g., long) to store the sum of tastiness values to prevent overflow. |
k (the maximum number of candies) is zero or negative | Return 0 as no candies can be selected, leading to zero tastiness. |
k is larger than the number of candies available | Adjust k to be the number of candies available to avoid out-of-bounds errors. |
Candies with zero or negative tastiness values. | The algorithm should handle these correctly; negative values will reduce the overall tastiness, and zero values will not contribute. |