Is Largest Values From Labels easy or hard?

Largest Values From Labels is rated Medium difficulty. The challenge lies in recognizing the greedy strategy and enforcing label constraints efficiently. Once the sorting insight is clear, the implementation becomes straightforward.

How to solve Largest Values From Labels in O(n)?

A true O(n) solution is generally not feasible because the algorithm needs to process items in descending value order. That ordering requires sorting or a heap, both of which take O(n log n) time. Without ordering, the greedy choice of the highest-value item cannot be guaranteed.

What is the best approach for Largest Values From Labels?

The best approach is greedy with sorting. Pair each value with its label, sort items by value in descending order, and select items while tracking how many times each label is used. This ensures you always pick the highest-value valid item while respecting the label limit. The complexity is O(n log n) due to sorting.

Is Largest Values From Labels asked at Google/Amazon/Meta?

Greedy selection problems with constraints frequently appear in interviews at companies like Amazon, Google, and Meta. While this exact problem may not always appear, similar patterns involving sorting by value and enforcing category limits are common interview questions.

What data structure is used in Largest Values From Labels?

The solution typically uses arrays for storing value-label pairs, a hash map or counting array to track how many times each label has been selected, and optionally a max heap (priority queue) to retrieve the largest value efficiently.

What is the time complexity of Largest Values From Labels?

The optimal solution runs in O(n log n) time because the items must be sorted by value or processed using a max heap. After sorting, a single pass selects up to numWanted elements while checking label counts. Space complexity is O(n) for storing pairs and label usage counts.

Largest Values From Labels Python or Java solution approach?

Both Python and Java implementations follow the same strategy: combine values and labels, sort by value descending, maintain a map of label usage, and accumulate values until numWanted items are selected. The logic is identical across languages; only syntax differs.

Description Hints 1 Solution Video 12:18

1090. Largest Values From Labels

Medium64.7% acceptance1 company

You are given n item's value and label as two integer arrays values and labels. You are also given two integers numWanted and useLimit.

Your task is to find a subset of items with the maximum sum of their values such that:

The number of items is at most numWanted.
The number of items with the same label is at most useLimit.

Return the maximum sum.

Example 1:

Input: values = [5,4,3,2,1], labels = [1,1,2,2,3], numWanted = 3, useLimit = 1

Output: 9

Explanation:

The subset chosen is the first, third, and fifth items with the sum of values 5 + 3 + 1.

Example 2:

Input: values = [5,4,3,2,1], labels = [1,3,3,3,2], numWanted = 3, useLimit = 2

Output: 12

Explanation:

The subset chosen is the first, second, and third items with the sum of values 5 + 4 + 3.

Example 3:

Input: values = [9,8,8,7,6], labels = [0,0,0,1,1], numWanted = 3, useLimit = 1

Output: 16

Explanation:

The subset chosen is the first and fourth items with the sum of values 9 + 7.

Constraints:

n == values.length == labels.length
1 <= n <= 2 * 10⁴
0 <= values[i], labels[i] <= 2 * 10⁴
1 <= numWanted, useLimit <= n

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Google

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Hint 1

Consider the items in order from largest to smallest value, and greedily take the items if they fall under the use_limit. We can keep track of how many items of each label are used by using a hash table.

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1090. Largest Values From Labels

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