Last Stone Weight

Last Stone Weight is a Easy Data Structures and Algorithms interview problem you can solve, run and submit on Thita.ai. It belongs to the Heap (Priority Queue) Patterns pattern, in the Top K Elements (Selection/Frequency) subpattern.

Problem statement

You are given an array of integers stones where stones[i] is the weight of the ith stone.

We are playing a game with the stones. On each turn, we choose the heaviest two stones and smash them together. Suppose the heaviest two stones have weights x and y with x <= y. The result of this smash is:

- If x == y, both stones are destroyed, and

- If x != y, the stone of weight x is destroyed, and the stone of weight y has new weight y - x.

At the end of the game, there is at most one stone left.

Return the weight of the last remaining stone. If there are no stones left, return 0.

Example 1:

Input: stones = [2,7,4,1,8,1] Output: 1 Explanation: We combine 7 and 8 to get 1 so the array converts to [2,4,1,1,1] then, we combine 2 and 4 to get 2 so the array converts to [2,1,1,1] then, we combine 2 and 1 to get 1 so the array converts to [1,1,1] then, we combine 1 and 1 to get 0 so the array converts to [1] then that's the value of the last stone.

Example 2:

Input: stones = [1] Output: 1

Constraints:

- 1 <= stones.length <= 30

- 1 <= stones[i] <= 1000

Topics and companies

Topics: Array, Heap (Priority Queue).

Reported in interviews at Amazon.

How to practise Last Stone Weight on Thita.ai

Starter code is provided in C, C#, C++, Go, Java, JavaScript and Python. Your solution runs against 5 test cases for this problem, with the AI coach available for a hint when you are stuck rather than a finished answer. The reference solution runs in O(nlogn) time and O(n) space.

Open Last Stone Weight in the code editor, or read the Last Stone Weight editorial for a worked solution with its approach and complexity analysis.

Where Last Stone Weight sits in the DSA pattern sheet

Problems related to Last Stone Weight

Other problems that use the same Top K Elements (Selection/Frequency) and Heap (Priority Queue) Patterns techniques:

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