Minimum Unique Material Identifiers — Problem Statement & Solution Guide
Quick Answer & Algorithm Key Takeaway
Master coding challenges related to Arrays and solve the Minimum Unique Material Identifiers problem optimally.
O(R + totalPairs)O(maxFrequency + totalPairsPerMaterial)Problem Description
Given a two‑dimensional integer array rooms where each inner array lists the material types present in one room, assign a numeric material identifier to every (room, material) pair. Identifiers may be reused across different material types, but for any fixed material type its identifier must be distinct in every room that contains it. Compute the smallest possible total number of distinct identifiers required. Input: rooms – list of lists of integers. Output: a single integer – the minimal number of identifiers needed.
DSA Pattern Breakdown
DSA Pattern Breakdown
"Minimum Unique Material Identifiers"
WHY DOES IT MATTER?
Efficient identifier allocation is a recurring pattern in systems that need compact naming (e.g., resource handles, port numbers). Mastering this pattern helps reduce memory footprint and improves cache locality.
OPTIMIZATION CHALLENGE
The breakthrough is to decouple materials and treat each as an independent coloring problem, allowing identifier reuse across materials and limiting the identifier pool to the highest per‑material frequency.
REAL-WORLD CONNECTION
Think of assigning channel numbers in a telecom network: each service (material) needs a unique channel per cell (room), but channels can be reused for different services in the same cell, mirroring the identifier reuse across material types.
When coding, first group positions by material, then iterate rooms and use a small boolean array or hash set to find the minimal free identifier – this keeps the inner loop O(1) on average.
COMPLEXITY AT A GLANCE
O(R + totalPairs)O(maxFrequency + totalPairsPerMaterial)Core Theory — Why This Approach?
The problem can be modeled as a variant of graph coloring where each material type acts like a color class that must receive distinct identifiers (colors) across the rooms it appears in. A naive solution would allocate a fresh identifier for every (room, material) pair, leading to O(N) distinct identifiers where N is the total number of pairs, which quickly becomes infeasible for large inputs. By recognizing that identifiers can be reused across different material types, we can treat each material independently and assign the smallest unused identifier within each room, similar to the greedy coloring of interval graphs. This reduces the identifier pool to the maximum frequency of any material across rooms, which is the optimal lower bound. The optimal paradigm therefore combines a per‑material greedy assignment with a hash‑set (or boolean array) to track used identifiers in the current room, achieving linear time overall.
Interview Questions on This Problem
Q1How would you design an algorithm to minimize the total number of distinct identifiers while ensuring each material type has unique identifiers across rooms?
Process each material type independently, and for each room that contains the material, assign the smallest identifier not yet used for that material in that room, using a hash set to track used identifiers per material. The total distinct identifiers equals the maximum count of any material across rooms.
Q2Why does a simple "assign a new ID to every (room, material) pair" approach fail for large datasets, and how can you improve it?
That approach yields O(totalPairs) distinct IDs and O(totalPairs) memory, which is unnecessary because identifiers can be reused across different materials. By reusing IDs and only enforcing uniqueness per material, we reduce both identifier count and memory to O(maxFrequency) using a greedy per‑material assignment.
Q3Can you relate this identifier‑minimization problem to a classic graph‑theoretic concept and explain the analogy?
It maps to graph coloring where each material forms a set of vertices (rooms) that must receive distinct colors; the goal is to use the fewest colors overall, which for interval‑like constraints is solved greedily by always picking the smallest available color.
Examples
Input
[[1,2],[2,3],[1,3,4]]
Output
2
Explanation: Material 1 occurs in rooms 0 and 2 → needs 2 different IDs. Material 2 occurs in rooms 0 and 1 → also 2 IDs. Material 3 occurs in rooms 1 and 2 → 2 IDs. Material 4 occurs once. The maximum occurrence count among all materials is 2, so we can reuse identifiers 1 and 2 for every material type, achieving the minimum of 2 distinct identifiers.
Input
[[5],[5],[5],[5]]
Output
4
Explanation: Material 5 appears in all four rooms, therefore each room must receive a unique identifier for material 5. No other material exists, so the minimum number of distinct identifiers equals 4.
Input
[[1,2,3],[4,5,6],[7,8,9]]
Output
1
Explanation: Every material type appears in exactly one room, so a single identifier can be reused for all types. The maximum frequency is 1, thus only one distinct identifier is sufficient.
Constraints
- 1 <= rooms.length <= 100000
- 0 <= rooms[i].length <= 2000
- -10^9 <= rooms[i][j] <= 10^9
- Total number of material entries across all rooms does not exceed 200000
Optimal Approach & Strategy
Group entries by material and, for each room, assign the smallest unused identifier for that material using a hash set or boolean array. This greedy per‑material strategy yields the minimal identifier pool.
Brute Force Approach
Assign a brand‑new identifier to every (room, material) pair, which guarantees uniqueness but creates O(totalPairs) distinct IDs. This blows up both time and space for large inputs.
Code Solutions
/**
* @param {number[][]} rooms
* @return {number}
*/
var minUniqueMaterialIdentifiers = function(rooms) {
// Count frequency of each material type across all rooms
const materialFreq = new Map();
for (const room of rooms) {
// Use a Set to handle duplicate materials within the same room
const uniqueMaterials = new Set(room);
for (const mat of uniqueMaterials) {
materialFreq.set(mat, (materialFreq.get(mat) || 0) + 1);
}
}
// The minimum number of distinct identifiers needed is the maximum frequency
// of any single material type.
let maxFreq = 0;
for (const count of materialFreq.values()) {
maxFreq = Math.max(maxFreq, count);
}
return maxFreq;
};
// Example usage
const rooms = [[1, 2], [2, 3], [1, 3, 4]];
console.log(minUniqueMaterialIdentifiers(rooms));#include <iostream>
#include <vector>
#include <unordered_map>
#include <unordered_set>
#include <algorithm>
using namespace std;
class Solution {
public:
int minUniqueMaterialIdentifiers(vector<vector<int>>& rooms) {
// Count frequency of each material type across all rooms
unordered_map<int, int> materialFreq;
for (const auto& room : rooms) {
unordered_set<int> uniqueMaterialsInRoom(room.begin(), room.end());
for (int mat : uniqueMaterialsInRoom) {
materialFreq[mat]++;
}
}
// The minimum number of distinct identifiers needed is the maximum frequency
// of any single material type, because each occurrence of that material in
// different rooms must have a distinct identifier.
int maxFreq = 0;
for (const auto& pair : materialFreq) {
maxFreq = max(maxFreq, pair.second);
}
return maxFreq;
}
};
int main() {
vector<vector<int>> rooms = {{1, 2}, {2, 3}, {1, 3, 4}};
Solution sol;
cout << sol.minUniqueMaterialIdentifiers(rooms) << endl;
return 0;
}import java.util.*;
class Solution {
public int minUniqueMaterialIdentifiers(int[][] rooms) {
// Count frequency of each material type across all rooms
Map<Integer, Integer> materialFreq = new HashMap<>();
for (int[] room : rooms) {
// Use a Set to handle duplicate materials within the same room
Set<Integer> uniqueMaterials = new HashSet<>(Arrays.asList(room));
for (int mat : uniqueMaterials) {
materialFreq.put(mat, materialFreq.getOrDefault(mat, 0) + 1);
}
}
// The minimum number of distinct identifiers needed is the maximum frequency
// of any single material type.
int maxFreq = 0;
for (int count : materialFreq.values()) {
maxFreq = Math.max(maxFreq, count);
}
return maxFreq;
}
public static void main(String[] args) {
int[][] rooms = {{1, 2}, {2, 3}, {1, 3, 4}};
Solution sol = new Solution();
System.out.println(sol.minUniqueMaterialIdentifiers(rooms));
}
}from typing import List
class Solution:
def minUniqueMaterialIdentifiers(self, rooms: List[List[int]]) -> int:
# Count frequency of each material type across all rooms
material_freq = {}
for room in rooms:
# Use a set to handle duplicate materials within the same room
unique_materials = set(room)
for mat in unique_materials:
material_freq[mat] = material_freq.get(mat, 0) + 1
# The minimum number of distinct identifiers needed is the maximum frequency
# of any single material type.
if not material_freq:
return 0
return max(material_freq.values())
# Example usage
if __name__ == "__main__":
sol = Solution()
rooms = [[1, 2], [2, 3], [1, 3, 4]]
print(sol.minUniqueMaterialIdentifiers(rooms))/**
* @param {number[][]} rooms
* @return {number}
*/
var minUniqueMaterialIdentifiers = function(rooms) {
// Count frequency of each material type across all rooms
const materialFreq = new Map();
for (const room of rooms) {
// Use a Set to handle duplicate materials within the same room
const uniqueMaterials = new Set(room);
for (const mat of uniqueMaterials) {
materialFreq.set(mat, (materialFreq.get(mat) || 0) + 1);
}
}
// The minimum number of distinct identifiers needed is the maximum frequency
// of any single material type.
let maxFreq = 0;
for (const count of materialFreq.values()) {
maxFreq = Math.max(maxFreq, count);
}
return maxFreq;
};
// Example usage
const rooms = [[1, 2], [2, 3], [1, 3, 4]];
console.log(minUniqueMaterialIdentifiers(rooms));Asked in Top Tech Interviews
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