Consumer buckets as cpi can be optimized with known problem of knapsack problem. You can optimize the selection of items to ensure that the most representative items are included within the given budget or space and create an automated dynamic array.

Here is how Grok 3 decribes the knapsack problem:

“Alright, let’s dive into the knapsack problem! It’s a classic in computer science and optimization—super interesting stuff. Essentially, you’ve got a set of items, each with a weight and a value, and a knapsack with a limited weight capacity. The goal is to figure out which items to pack to maximize the total value without busting the weight limit. There are a few flavors of it, but I’ll assume you’re asking about the 0/1 knapsack problem (where you either take an item or leave it, no fractions). If you meant a different version, just let me know!

Here’s the gist:

Input: A list of items with weights (e.g., [2, 3, 4]) and values (e.g., [3, 4, 5]), plus a max weight capacity (e.g., 7).
Output: The maximum value you can achieve and usually which items to pick.
How It Works
The most straightforward way to solve it is with dynamic programming (DP), since trying every possible combination (brute force) takes exponential time—like 2^n, where n is the number of items. DP brings it down to something more manageable: O(nW), where W is the capacity.

Imagine a table where rows are the items and columns are weights from 0 to the capacity. Each cell [i][w] represents the…