Is Divide Chocolate easy or hard?

Divide Chocolate is classified as a Hard problem because it requires recognizing the 'binary search on the answer' pattern. Once that insight is clear, the greedy feasibility check becomes straightforward, but identifying the pattern is the main challenge.

Divide Chocolate Python/Java solution

Most implementations follow the same pattern: binary search the minimum sweetness value and validate it with a greedy pass through the array. The logic is identical across Python, Java, C++, Go, and TypeScript with O(n log S) time complexity.

How to solve Divide Chocolate in O(n)?

A pure O(n) algorithm is not possible because the minimum sweetness value must be searched across a range of possible answers. The closest optimal complexity is O(n log S) using binary search on the answer combined with a linear greedy validation pass.

What is the best approach for Divide Chocolate?

The optimal approach uses Binary Search combined with a Greedy feasibility check. Binary search guesses the minimum sweetness you can guarantee for yourself, while the greedy pass verifies if the chocolate can be split into at least k+1 pieces with each piece having at least that sweetness. This runs in O(n log S) time where S is the total sweetness.

Is Divide Chocolate asked at Google/Amazon/Meta?

Divide Chocolate is a common interview-style problem involving binary search on the answer and greedy validation. Variants of this pattern appear in interviews at companies like Google and Amazon when testing candidates on optimization and partitioning strategies.

What data structure is used in Divide Chocolate?

The problem primarily uses arrays along with a greedy accumulation technique. Binary search is applied over the possible answer range, and a running sum across the array determines when a valid chocolate segment can be formed.

What is the time complexity of Divide Chocolate?

The optimal Binary Search + Greedy solution runs in O(n log S) time and O(1) space. Each binary search step scans the array once to check if k+1 valid pieces can be formed, and the search range depends on the total sweetness sum.

Description Hints 3 Solution Video 16:44

1231. Divide Chocolate

Hard60.5% acceptance1 companyPremium elsewhere, free here

You have one chocolate bar that consists of some chunks. Each chunk has its own sweetness given by the array sweetness.

You want to share the chocolate with your k friends so you start cutting the chocolate bar into k + 1 pieces using k cuts, each piece consists of some consecutive chunks.

Being generous, you will eat the piece with the minimum total sweetness and give the other pieces to your friends.

Find the maximum total sweetness of the piece you can get by cutting the chocolate bar optimally.

Example 1:

Input: sweetness = [1,2,3,4,5,6,7,8,9], k = 5
Output: 6
Explanation: You can divide the chocolate to [1,2,3], [4,5], [6], [7], [8], [9]

Example 2:

Input: sweetness = [5,6,7,8,9,1,2,3,4], k = 8
Output: 1
Explanation: There is only one way to cut the bar into 9 pieces.

Example 3:

Input: sweetness = [1,2,2,1,2,2,1,2,2], k = 2
Output: 5
Explanation: You can divide the chocolate to [1,2,2], [1,2,2], [1,2,2]

Constraints:

0 <= k < sweetness.length <= 10⁴
1 <= sweetness[i] <= 10⁵

Asked at 1 company

Google

Topics

Array Binary Search

Hints open one at a time

Hint 1

After dividing the array into K+1 sub-arrays, you will pick the sub-array with the minimum sum.

Hint 2

Divide the sub-array into K+1 sub-arrays such that the minimum sub-array sum is as maximum as possible.

Hint 3

Use binary search with greedy check.

16:44

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