LeetCode #2151 — HARD

Maximum Good People Based on Statements

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

There are two types of persons:

  • The good person: The person who always tells the truth.
  • The bad person: The person who might tell the truth and might lie.

You are given a 0-indexed 2D integer array statements of size n x n that represents the statements made by n people about each other. More specifically, statements[i][j] could be one of the following:

  • 0 which represents a statement made by person i that person j is a bad person.
  • 1 which represents a statement made by person i that person j is a good person.
  • 2 represents that no statement is made by person i about person j.

Additionally, no person ever makes a statement about themselves. Formally, we have that statements[i][i] = 2 for all 0 <= i < n.

Return the maximum number of people who can be good based on the statements made by the n people.

Example 1:

Input: statements = [[2,1,2],[1,2,2],[2,0,2]]
Output: 2
Explanation: Each person makes a single statement.
- Person 0 states that person 1 is good.
- Person 1 states that person 0 is good.
- Person 2 states that person 1 is bad.
Let's take person 2 as the key.
- Assuming that person 2 is a good person:
    - Based on the statement made by person 2, person 1 is a bad person.
    - Now we know for sure that person 1 is bad and person 2 is good.
    - Based on the statement made by person 1, and since person 1 is bad, they could be:
        - telling the truth. There will be a contradiction in this case and this assumption is invalid.
        - lying. In this case, person 0 is also a bad person and lied in their statement.
    - Following that person 2 is a good person, there will be only one good person in the group.
- Assuming that person 2 is a bad person:
    - Based on the statement made by person 2, and since person 2 is bad, they could be:
        - telling the truth. Following this scenario, person 0 and 1 are both bad as explained before.
            - Following that person 2 is bad but told the truth, there will be no good persons in the group.
        - lying. In this case person 1 is a good person.
            - Since person 1 is a good person, person 0 is also a good person.
            - Following that person 2 is bad and lied, there will be two good persons in the group.
We can see that at most 2 persons are good in the best case, so we return 2.
Note that there is more than one way to arrive at this conclusion.

Example 2:

Input: statements = [[2,0],[0,2]]
Output: 1
Explanation: Each person makes a single statement.
- Person 0 states that person 1 is bad.
- Person 1 states that person 0 is bad.
Let's take person 0 as the key.
- Assuming that person 0 is a good person:
    - Based on the statement made by person 0, person 1 is a bad person and was lying.
    - Following that person 0 is a good person, there will be only one good person in the group.
- Assuming that person 0 is a bad person:
    - Based on the statement made by person 0, and since person 0 is bad, they could be:
        - telling the truth. Following this scenario, person 0 and 1 are both bad.
            - Following that person 0 is bad but told the truth, there will be no good persons in the group.
        - lying. In this case person 1 is a good person.
            - Following that person 0 is bad and lied, there will be only one good person in the group.
We can see that at most, one person is good in the best case, so we return 1.
Note that there is more than one way to arrive at this conclusion.

Constraints:

  • n == statements.length == statements[i].length
  • 2 <= n <= 15
  • statements[i][j] is either 0, 1, or 2.
  • statements[i][i] == 2
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: There are two types of persons: The good person: The person who always tells the truth. The bad person: The person who might tell the truth and might lie. You are given a 0-indexed 2D integer array statements of size n x n that represents the statements made by n people about each other. More specifically, statements[i][j] could be one of the following: 0 which represents a statement made by person i that person j is a bad person. 1 which represents a statement made by person i that person j is a good person. 2 represents that no statement is made by person i about person j. Additionally, no person ever makes a statement about themselves. Formally, we have that statements[i][i] = 2 for all 0 <= i < n. Return the maximum number of people who can be good based on the statements made by the n people.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Backtracking · Bit Manipulation

Example 1

[[2,1,2],[1,2,2],[2,0,2]]

Example 2

[[2,0],[0,2]]

Related Problems

  • Maximum Score Words Formed by Letters (maximum-score-words-formed-by-letters)
Step 02

Core Insight

What unlocks the optimal approach

  • You should test every possible assignment of good and bad people, using a bitmask.
  • In each bitmask, if the person i is good, then his statements should be consistent with the bitmask in order for the assignment to be valid.
  • If the assignment is valid, count how many people are good and keep track of the maximum.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2151: Maximum Good People Based on Statements
class Solution {
    public int maximumGood(int[][] statements) {
        int ans = 0;
        for (int mask = 1; mask < 1 << statements.length; ++mask) {
            ans = Math.max(ans, check(mask, statements));
        }
        return ans;
    }

    private int check(int mask, int[][] statements) {
        int cnt = 0;
        int n = statements.length;
        for (int i = 0; i < n; ++i) {
            if (((mask >> i) & 1) == 1) {
                for (int j = 0; j < n; ++j) {
                    int v = statements[i][j];
                    if (v < 2 && ((mask >> j) & 1) != v) {
                        return 0;
                    }
                }
                ++cnt;
            }
        }
        return cnt;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n!)
Space
O(n)

Approach Breakdown

EXHAUSTIVE
O(nⁿ) time
O(n) space

Generate every possible combination without any filtering. At each of n positions we choose from up to n options, giving nⁿ total candidates. Each candidate takes O(n) to validate. No pruning means we waste time on clearly invalid partial solutions.

BACKTRACKING + PRUNING
O(n!) time
O(n) space

Backtracking explores a decision tree, but prunes branches that violate constraints early. Worst case is still factorial or exponential, but pruning dramatically reduces the constant factor in practice. Space is the recursion depth (usually O(n) for n-level decisions).

Shortcut: Backtracking time = size of the pruned search tree. Focus on proving your pruning eliminates most branches.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

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.

Missing undo step on backtrack

Wrong move: Mutable state leaks between branches.

Usually fails on: Later branches inherit selections from earlier branches.

Fix: Always revert state changes immediately after recursive call.