#2931

Hard

auto_awesomeGreedy choice plus invariant validation

LeetCode Problem Workspace

Maximum Spending After Buying Items

Maximize spending by carefully choosing the right items across multiple shops over m * n days.

array greedy sorting heap priority queue matrix

Problem Statement

You are given a 0-indexed m * n integer matrix 'values', where each row represents a shop, and each item in a row has a distinct value. The matrix is sorted such that values[i][j] >= values[i][j + 1] for all 0 <= j < n - 1, meaning that the items in each shop are listed in non-increasing order.

On each day, you are allowed to buy one item from one of the shops. The objective is to maximize the total cost of items bought across m * n days. On day d, you buy an item from the shop with the highest available value, while considering the day’s multiplier. The challenge is to make the right choices to maximize the spending.

Examples

Example 1

Input: values = [[8,5,2],[6,4,1],[9,7,3]]

Output: 285

On the first day, we buy product 2 from shop 1 for a price of values[1][2] * 1 = 1. On the second day, we buy product 2 from shop 0 for a price of values[0][2] * 2 = 4. On the third day, we buy product 2 from shop 2 for a price of values[2][2] * 3 = 9. On the fourth day, we buy product 1 from shop 1 for a price of values[1][1] * 4 = 16. On the fifth day, we buy product 1 from shop 0 for a price of values[0][1] * 5 = 25. On the sixth day, we buy product 0 from shop 1 for a price of values[1][0] * 6 = 36. On the seventh day, we buy product 1 from shop 2 for a price of values[2][1] * 7 = 49. On the eighth day, we buy product 0 from shop 0 for a price of values[0][0] * 8 = 64. On the ninth day, we buy product 0 from shop 2 for a price of values[2][0] * 9 = 81. Hence, our total spending is equal to 285. It can be shown that 285 is the maximum amount of money that can be spent buying all m * n products.

Example 2

Input: values = [[10,8,6,4,2],[9,7,5,3,2]]

Output: 386

On the first day, we buy product 4 from shop 0 for a price of values[0][4] * 1 = 2. On the second day, we buy product 4 from shop 1 for a price of values[1][4] * 2 = 4. On the third day, we buy product 3 from shop 1 for a price of values[1][3] * 3 = 9. On the fourth day, we buy product 3 from shop 0 for a price of values[0][3] * 4 = 16. On the fifth day, we buy product 2 from shop 1 for a price of values[1][2] * 5 = 25. On the sixth day, we buy product 2 from shop 0 for a price of values[0][2] * 6 = 36. On the seventh day, we buy product 1 from shop 1 for a price of values[1][1] * 7 = 49. On the eighth day, we buy product 1 from shop 0 for a price of values[0][1] * 8 = 64 On the ninth day, we buy product 0 from shop 1 for a price of values[1][0] * 9 = 81. On the tenth day, we buy product 0 from shop 0 for a price of values[0][0] * 10 = 100. Hence, our total spending is equal to 386. It can be shown that 386 is the maximum amount of money that can be spent buying all m * n products.

Constraints

1 <= m == values.length <= 10
1 <= n == values[i].length <= 104
1 <= values[i][j] <= 106
values[i] are sorted in non-increasing order.

Solution Approach

Greedy Item Selection

The core approach is to iterate through the days and, on each day, select the item that maximizes the total spending. This requires taking the item with the highest value from the available choices across the shops, while factoring in the day multiplier to adjust the cost.

Using a Priority Queue

A heap (priority queue) can be utilized to efficiently choose the highest value items. By maintaining a heap for each shop, you can easily retrieve the item with the highest value available at any point during the iteration.

Day-wise Multiplier Application

The total cost on each day depends not only on the item selected but also on the day multiplier. As you iterate through the m * n days, the value of each item must be multiplied by its respective day number to compute the total spending.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

Time complexity depends on the final approach. Using a priority queue for each shop leads to an overall time complexity of O(m * n log n), where m is the number of shops and n is the number of items per shop. Space complexity is also O(m * n) to store the heap structures for each shop.

What Interviewers Usually Probe

Can the candidate apply greedy algorithms in a structured, problem-specific way?
Does the candidate efficiently utilize data structures like heaps or priority queues?
Can the candidate balance between greedy choice and maintaining invariant properties?

Common Pitfalls or Variants

Common pitfalls

Selecting items without considering the day multiplier can lead to suboptimal solutions.
Not maintaining the correct heap structure or failing to efficiently pop the maximum values from the heap.
Ignoring the constraints that each item is unique across all shops, which may lead to confusion when selecting items.

Follow-up variants

What if the number of shops or items is much larger? Would the solution scale efficiently?
Can this problem be adapted to allow purchasing multiple items per day, or is it strictly one item per day?
How would the problem change if the values were sorted in increasing order instead of non-increasing order?

FAQ

How do I approach the greedy part of 'Maximum Spending After Buying Items'?

Start by selecting the highest value item from the available options, while considering the day multiplier that increases with each iteration.

What data structure can I use to implement the greedy algorithm efficiently?

Using a heap (priority queue) is an effective choice for maintaining the highest value items while iterating over the days.

Why does sorting matter in the problem 'Maximum Spending After Buying Items'?

The sorting of items within each shop allows us to make efficient choices using a greedy approach, ensuring that we can select the best item each day.

How does the multiplier affect the solution in this problem?

Each day's multiplier makes the cost of selecting an item increase with each passing day, which must be taken into account when selecting the best items.

What are the key challenges in solving this problem?

The main challenge lies in balancing between greedy choices and maintaining an efficient selection process using data structures like heaps, while respecting the day multiplier.

terminal

Solution

Solution 1: Greedy + Priority Queue

According to the problem description, we should prioritize purchasing items with smaller values and leave items with larger values to be purchased later in order to maximize the total cost. Therefore, we use a priority queue (min-heap) to store the smallest value item that has not been purchased in each store. Initially, we add the rightmost item in each store to the priority queue.

class Solution:
    def maxSpending(self, values: List[List[int]]) -> int:
        n = len(values[0])
        pq = [(row[-1], i, n - 1) for i, row in enumerate(values)]
        heapify(pq)
        ans = d = 0
        while pq:
            d += 1
            v, i, j = heappop(pq)
            ans += v * d
            if j:
                heappush(pq, (values[i][j - 1], i, j - 1))
        return ans

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