Off-by-one on range boundaries
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Move from brute-force thinking to an efficient approach using array strategy.
You are given an integer n indicating there are n specialty retail stores. There are m product types of varying amounts, which are given as a 0-indexed integer array quantities, where quantities[i] represents the number of products of the ith product type.
You need to distribute all products to the retail stores following these rules:
0). Let x represent the maximum number of products given to any store. You want x to be as small as possible, i.e., you want to minimize the maximum number of products that are given to any store.Return the minimum possible x.
Example 1:
Input: n = 6, quantities = [11,6] Output: 3 Explanation: One optimal way is: - The 11 products of type 0 are distributed to the first four stores in these amounts: 2, 3, 3, 3 - The 6 products of type 1 are distributed to the other two stores in these amounts: 3, 3 The maximum number of products given to any store is max(2, 3, 3, 3, 3, 3) = 3.
Example 2:
Input: n = 7, quantities = [15,10,10] Output: 5 Explanation: One optimal way is: - The 15 products of type 0 are distributed to the first three stores in these amounts: 5, 5, 5 - The 10 products of type 1 are distributed to the next two stores in these amounts: 5, 5 - The 10 products of type 2 are distributed to the last two stores in these amounts: 5, 5 The maximum number of products given to any store is max(5, 5, 5, 5, 5, 5, 5) = 5.
Example 3:
Input: n = 1, quantities = [100000] Output: 100000 Explanation: The only optimal way is: - The 100000 products of type 0 are distributed to the only store. The maximum number of products given to any store is max(100000) = 100000.
Constraints:
m == quantities.length1 <= m <= n <= 1051 <= quantities[i] <= 105Problem summary: You are given an integer n indicating there are n specialty retail stores. There are m product types of varying amounts, which are given as a 0-indexed integer array quantities, where quantities[i] represents the number of products of the ith product type. You need to distribute all products to the retail stores following these rules: A store can only be given at most one product type but can be given any amount of it. After distribution, each store will have been given some number of products (possibly 0). Let x represent the maximum number of products given to any store. You want x to be as small as possible, i.e., you want to minimize the maximum number of products that are given to any store. Return the minimum possible x.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Binary Search · Greedy
6 [11,6]
7 [15,10,10]
1 [100000]
koko-eating-bananas)capacity-to-ship-packages-within-d-days)maximum-candies-allocated-to-k-children)find-the-smallest-divisor-given-a-threshold)magnetic-force-between-two-balls)Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #2064: Minimized Maximum of Products Distributed to Any Store
class Solution {
public int minimizedMaximum(int n, int[] quantities) {
int left = 1, right = (int) 1e5;
while (left < right) {
int mid = (left + right) >> 1;
int cnt = 0;
for (int v : quantities) {
cnt += (v + mid - 1) / mid;
}
if (cnt <= n) {
right = mid;
} else {
left = mid + 1;
}
}
return left;
}
}
// Accepted solution for LeetCode #2064: Minimized Maximum of Products Distributed to Any Store
func minimizedMaximum(n int, quantities []int) int {
return 1 + sort.Search(1e5, func(x int) bool {
x++
cnt := 0
for _, v := range quantities {
cnt += (v + x - 1) / x
}
return cnt <= n
})
}
# Accepted solution for LeetCode #2064: Minimized Maximum of Products Distributed to Any Store
class Solution:
def minimizedMaximum(self, n: int, quantities: List[int]) -> int:
def check(x):
return sum((v + x - 1) // x for v in quantities) <= n
return 1 + bisect_left(range(1, 10**6), True, key=check)
// Accepted solution for LeetCode #2064: Minimized Maximum of Products Distributed to Any Store
/**
* [2064] Minimized Maximum of Products Distributed to Any Store
*
* You are given an integer n indicating there are n specialty retail stores. There are m product types of varying amounts, which are given as a 0-indexed integer array quantities, where quantities[i] represents the number of products of the i^th product type.
* You need to distribute all products to the retail stores following these rules:
*
* A store can only be given at most one product type but can be given any amount of it.
* After distribution, each store will have been given some number of products (possibly 0). Let x represent the maximum number of products given to any store. You want x to be as small as possible, i.e., you want to minimize the maximum number of products that are given to any store.
*
* Return the minimum possible x.
*
* Example 1:
*
* Input: n = 6, quantities = [11,6]
* Output: 3
* Explanation: One optimal way is:
* - The 11 products of type 0 are distributed to the first four stores in these amounts: 2, 3, 3, 3
* - The 6 products of type 1 are distributed to the other two stores in these amounts: 3, 3
* The maximum number of products given to any store is max(2, 3, 3, 3, 3, 3) = 3.
*
* Example 2:
*
* Input: n = 7, quantities = [15,10,10]
* Output: 5
* Explanation: One optimal way is:
* - The 15 products of type 0 are distributed to the first three stores in these amounts: 5, 5, 5
* - The 10 products of type 1 are distributed to the next two stores in these amounts: 5, 5
* - The 10 products of type 2 are distributed to the last two stores in these amounts: 5, 5
* The maximum number of products given to any store is max(5, 5, 5, 5, 5, 5, 5) = 5.
*
* Example 3:
*
* Input: n = 1, quantities = [100000]
* Output: 100000
* Explanation: The only optimal way is:
* - The 100000 products of type 0 are distributed to the only store.
* The maximum number of products given to any store is max(100000) = 100000.
*
*
* Constraints:
*
* m == quantities.length
* 1 <= m <= n <= 10^5
* 1 <= quantities[i] <= 10^5
*
*/
pub struct Solution {}
// problem: https://leetcode.com/problems/minimized-maximum-of-products-distributed-to-any-store/
// discuss: https://leetcode.com/problems/minimized-maximum-of-products-distributed-to-any-store/discuss/?currentPage=1&orderBy=most_votes&query=
// submission codes start here
impl Solution {
pub fn minimized_maximum(n: i32, quantities: Vec<i32>) -> i32 {
0
}
}
// submission codes end
#[cfg(test)]
mod tests {
use super::*;
#[test]
#[ignore]
fn test_2064_example_1() {
let n = 6;
let quantities = vec![11, 6];
let result = 3;
assert_eq!(Solution::minimized_maximum(n, quantities), result);
}
#[test]
#[ignore]
fn test_2064_example_2() {
let n = 7;
let quantities = vec![15, 10, 10];
let result = 5;
assert_eq!(Solution::minimized_maximum(n, quantities), result);
}
#[test]
#[ignore]
fn test_2064_example_3() {
let n = 1;
let quantities = vec![100000];
let result = 100000;
assert_eq!(Solution::minimized_maximum(n, quantities), result);
}
}
// Accepted solution for LeetCode #2064: Minimized Maximum of Products Distributed to Any Store
function minimizedMaximum(n: number, quantities: number[]): number {
let left = 1;
let right = 1e5;
while (left < right) {
const mid = (left + right) >> 1;
let cnt = 0;
for (const v of quantities) {
cnt += Math.ceil(v / mid);
}
if (cnt <= n) {
right = mid;
} else {
left = mid + 1;
}
}
return left;
}
Use this to step through a reusable interview workflow for this problem.
Check every element from left to right until we find the target or exhaust the array. Each comparison is O(1), and we may visit all n elements, giving O(n). No extra space needed.
Each comparison eliminates half the remaining search space. After k comparisons, the space is n/2ᵏ. We stop when the space is 1, so k = log₂ n. No extra memory needed — just two pointers (lo, hi).
Review these before coding to avoid predictable interview regressions.
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Wrong move: Setting `lo = mid` or `hi = mid` can stall and create an infinite loop.
Usually fails on: Two-element ranges never converge.
Fix: Use `lo = mid + 1` or `hi = mid - 1` where appropriate.
Wrong move: Locally optimal choices may fail globally.
Usually fails on: Counterexamples appear on crafted input orderings.
Fix: Verify with exchange argument or monotonic objective before committing.