Richest Customer Wealth - Video Solutions

Problem Overview: You receive a 2D matrix where accounts[i][j] represents the money the i-th customer has in the j-th bank. The task is to compute each customer's total wealth (sum of their row) and return the maximum wealth among all customers.

Approach 1: Iterative Calculation (O(m*n) time, O(1) space)

The most direct solution iterates through every customer row and calculates the sum of their accounts. For each row in the matrix, accumulate values using a simple loop and track the maximum sum encountered so far. This works because each row represents one customer's wealth distribution across banks. After computing the row total, compare it with the current maximum and update if necessary. The algorithm scans every cell exactly once, giving O(m*n) time complexity where m is the number of customers and n is the number of banks. Since only a few variables are used for tracking totals, the space complexity remains O(1). This approach relies on basic traversal of a array and is the most common interview implementation.

Approach 2: Matrix Map-Reduce Pattern (O(m*n) time, O(m) space)

Languages with strong functional utilities allow a compact solution using a map-reduce style pipeline. First compute the sum of each row using operations like sum() or reduce(). This produces an intermediate list where each value represents a customer's total wealth. Then apply a max() operation to find the richest customer. Conceptually, this is a two-step process: map each row to its total wealth, then reduce the results to the largest value. The time complexity remains O(m*n) because every element of the matrix must still be processed. Space complexity becomes O(m) if the intermediate wealth list is stored. This style is common in Python and JavaScript and demonstrates familiarity with functional transformations on collections.

Recommended for interviews: The iterative row-sum approach is what interviewers typically expect. It clearly shows you understand how to traverse a 2D array and maintain running aggregates. The map-reduce variant is concise and expressive in languages that support it well, but interviews usually prioritize the explicit loop implementation because it demonstrates control over iteration and memory usage.