You are given an m x n integer grid accounts where accounts[i][j] is the amount of money the ith customer has in the jth bank. Return the wealth that the richest customer has.
A customer's wealth is the amount of money they have in all their bank accounts. The richest customer is the customer that has the maximum wealth.
Example 1:
Input: accounts = [[1,2,3],[3,2,1]] Output: 6 Explanation:1st customer has wealth = 1 + 2 + 3 = 62nd customer has wealth = 3 + 2 + 1 = 6Both customers are considered the richest with a wealth of 6 each, so return 6.
Example 2:
Input: accounts = [[1,5],[7,3],[3,5]] Output: 10 Explanation: 1st customer has wealth = 6 2nd customer has wealth = 10 3rd customer has wealth = 8 The 2nd customer is the richest with a wealth of 10.
Example 3:
Input: accounts = [[2,8,7],[7,1,3],[1,9,5]] Output: 17
Constraints:
m == accounts.lengthn == accounts[i].length1 <= m, n <= 501 <= accounts[i][j] <= 100This approach involves iterating over each customer's accounts, calculating their total wealth by summing up the amounts, and keeping track of the maximum wealth found during these calculations. This method is simple and direct, leveraging basic iteration and comparison.
The code sums each row's elements in the accounts grid to calculate the wealth of each customer. The variable maxWealth keeps track of the highest wealth encountered.
C++
Java
Python
C#
JavaScript
Time Complexity: O(m * n), where m is the number of customers and n is the number of banks.
Space Complexity: O(1), as we only use a few extra variables irrespective of input size.
This approach considers a more functional programming style by mapping over the initial list to compute each customer's wealth, thereby generating an array of wealth values, which is in turn reduced (or simply searched) to obtain the maximum wealth.
Here, map is used to apply the sum function across all customers in accounts, and then max finds the highest resulting sum.
JavaScript
Java
Time Complexity: O(m * n)
Space Complexity: O(m)
| Approach | Complexity |
|---|---|
| Iterative Calculation Approach | Time Complexity: O(m * n), where m is the number of customers and n is the number of banks. |
| Matrix Utilization via Map-Reduce | Time Complexity: O(m * n) |
Richest Customer Wealth | Leetcode 1672 | Array • Ayushi Sharma • 9,240 views views
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