Is Arranging Coins easy or hard?

Arranging Coins is classified as an Easy problem on LeetCode. The basic greedy solution is straightforward, while the binary search optimization introduces a small algorithmic insight that often appears in beginner interview practice.

Arranging Coins Python or Java solution?

A typical Python or Java solution either iteratively subtracts row sizes or performs binary search using the formula k(k+1)/2. Both implementations run in constant space, while binary search achieves O(log n) time for large inputs.

How to solve Arranging Coins in O(log n)?

Use binary search on the number of possible rows k. Check whether k(k+1)/2 is less than or equal to n to determine if k rows can be formed. Adjust the search range accordingly until the largest valid k is found, resulting in O(log n) time and O(1) space.

What is the best approach for Arranging Coins?

Binary search is generally considered the best approach. It finds the largest k such that k(k+1)/2 <= n by searching the answer space instead of checking rows sequentially. This reduces the time complexity to O(log n) while using constant O(1) space.

What data structure is used in Arranging Coins?

No specialized data structure is required. The problem primarily relies on arithmetic calculations and optionally binary search over an integer range. The focus is on mathematical insight rather than arrays, hash maps, or trees.

What is the time complexity of Arranging Coins?

The greedy iterative approach runs in O(sqrt(n)) time because the number of rows grows roughly with the square root of n. A binary search optimization reduces the complexity to O(log n) by searching for the maximum valid row count. Both approaches use O(1) space.

Is Arranging Coins asked at Google, Amazon, or Meta?

Arranging Coins appears frequently in technical interviews because it tests mathematical reasoning and binary search. Variants of the problem have appeared in interviews at companies like Amazon and Google, especially for entry-level or early interview rounds.

Description Solution Video 12:53

441. Arranging Coins

Q: What data structure is used in Arranging Coins?

No specialized data structure is required. The problem primarily relies on arithmetic calculations and optionally binary search over an integer range. The focus is on mathematical insight rather than arrays, hash maps, or trees.

Q: What is the time complexity of Arranging Coins?

The greedy iterative approach runs in O(sqrt(n)) time because the number of rows grows roughly with the square root of n. A binary search optimization reduces the complexity to O(log n) by searching for the maximum valid row count. Both approaches use O(1) space.

Q: Is Arranging Coins asked at Google, Amazon, or Meta?

Arranging Coins appears frequently in technical interviews because it tests mathematical reasoning and binary search. Variants of the problem have appeared in interviews at companies like Amazon and Google, especially for entry-level or early interview rounds.

Easy48.7% acceptance6 companies

You have n coins and you want to build a staircase with these coins. The staircase consists of k rows where the i^th row has exactly i coins. The last row of the staircase may be incomplete.

Given the integer n, return the number of complete rows of the staircase you will build.

Example 1:

Input: n = 5
Output: 2
Explanation: Because the 3^rd row is incomplete, we return 2.

Example 2:

Input: n = 8
Output: 3
Explanation: Because the 4^th row is incomplete, we return 3.

Constraints:

1 <= n <= 2³¹ - 1

Asked at 6 companies

GoDaddy Bloomberg Microsoft Meta

Google Amazon

Topics

Binary Search Math

12:53

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1class Solution:
2    def arrangeCoins(self, n: int) -> int:
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