LeetCode #2712 — MEDIUM

Minimum Cost to Make All Characters Equal

Move from brute-force thinking to an efficient approach using dynamic programming strategy.

Solve on LeetCode

The Problem

Problem Statement

You are given a 0-indexed binary string s of length n on which you can apply two types of operations:

Choose an index i and invert all characters from index 0 to index i (both inclusive), with a cost of i + 1
Choose an index i and invert all characters from index i to index n - 1 (both inclusive), with a cost of n - i

Return the minimum cost to make all characters of the string equal.

Invert a character means if its value is '0' it becomes '1' and vice-versa.

Example 1:

Input: s = "0011"
Output: 2
Explanation: Apply the second operation with i = 2 to obtain s = "0000" for a cost of 2. It can be shown that 2 is the minimum cost to make all characters equal.

Example 2:

Input: s = "010101"
Output: 9
Explanation: Apply the first operation with i = 2 to obtain s = "101101" for a cost of 3.
Apply the first operation with i = 1 to obtain s = "011101" for a cost of 2. 
Apply the first operation with i = 0 to obtain s = "111101" for a cost of 1. 
Apply the second operation with i = 4 to obtain s = "111110" for a cost of 2.
Apply the second operation with i = 5 to obtain s = "111111" for a cost of 1. 
The total cost to make all characters equal is 9. It can be shown that 9 is the minimum cost to make all characters equal.

Constraints:

1 <= s.length == n <= 10⁵
s[i] is either '0' or '1'

Step 01

Brute Force Baseline

Problem summary: You are given a 0-indexed binary string s of length n on which you can apply two types of operations: Choose an index i and invert all characters from index 0 to index i (both inclusive), with a cost of i + 1 Choose an index i and invert all characters from index i to index n - 1 (both inclusive), with a cost of n - i Return the minimum cost to make all characters of the string equal. Invert a character means if its value is '0' it becomes '1' and vice-versa.

Baseline thinking

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

Pattern signal: Dynamic Programming · Greedy

Example 1

"0011"

Example 2

"010101"

Core Insight

What unlocks the optimal approach

For every index i, calculate the number of operations required to make the prefix [0, i - 1] equal to the character at index i, denoted prefix[i].
For every index i, calculate the number of operations required to make the suffix [i + 1, n - 1] equal to the character at index i, denoted suffix[i].
The final string will contain at least one character that is left unchanged; Therefore, the answer is the minimum of prefix[i] + suffix[i] for every i in [0, n - 1].

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
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

Upper-end input sizes

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 #2712: Minimum Cost to Make All Characters Equal
class Solution {
    public long minimumCost(String s) {
        long ans = 0;
        int n = s.length();
        for (int i = 1; i < n; ++i) {
            if (s.charAt(i) != s.charAt(i - 1)) {
                ans += Math.min(i, n - i);
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2712: Minimum Cost to Make All Characters Equal
func minimumCost(s string) (ans int64) {
	n := len(s)
	for i := 1; i < n; i++ {
		if s[i] != s[i-1] {
			ans += int64(min(i, n-i))
		}
	}
	return
}

# Accepted solution for LeetCode #2712: Minimum Cost to Make All Characters Equal
class Solution:
    def minimumCost(self, s: str) -> int:
        ans, n = 0, len(s)
        for i in range(1, n):
            if s[i] != s[i - 1]:
                ans += min(i, n - i)
        return ans

// Accepted solution for LeetCode #2712: Minimum Cost to Make All Characters Equal
impl Solution {
    pub fn minimum_cost(s: String) -> i64 {
        let mut ans = 0;
        let n = s.len();
        let s = s.as_bytes();
        for i in 1..n {
            if s[i] != s[i - 1] {
                ans += i.min(n - i);
            }
        }
        ans as i64
    }
}

// Accepted solution for LeetCode #2712: Minimum Cost to Make All Characters Equal
function minimumCost(s: string): number {
    let ans = 0;
    const n = s.length;
    for (let i = 1; i < n; ++i) {
        if (s[i] !== s[i - 1]) {
            ans += Math.min(i, n - i);
        }
    }
    return ans;
}

Step 06

Interactive Study Demo

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

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n × m)

Space

O(n × m)

Approach Breakdown

RECURSIVE

O(2ⁿ) time

O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING

O(n × m) time

O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.

Using greedy without proof

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.