LeetCode #3003 — HARD

Maximize the Number of Partitions After Operations

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

Solve on LeetCode

The Problem

Problem Statement

You are given a string s and an integer k.

First, you are allowed to change at most one index in s to another lowercase English letter.

After that, do the following partitioning operation until s is empty:

Choose the longest prefix of s containing at most k distinct characters.
Delete the prefix from s and increase the number of partitions by one. The remaining characters (if any) in s maintain their initial order.

Return an integer denoting the maximum number of resulting partitions after the operations by optimally choosing at most one index to change.

Example 1:

Input: s = "accca", k = 2

Output: 3

Explanation:

The optimal way is to change s[2] to something other than a and c, for example, b. then it becomes "acbca".

Then we perform the operations:

The longest prefix containing at most 2 distinct characters is "ac", we remove it and s becomes "bca".
Now The longest prefix containing at most 2 distinct characters is "bc", so we remove it and s becomes "a".
Finally, we remove "a" and s becomes empty, so the procedure ends.

Doing the operations, the string is divided into 3 partitions, so the answer is 3.

Example 2:

Input: s = "aabaab", k = 3

Output: 1

Explanation:

Initially s contains 2 distinct characters, so whichever character we change, it will contain at most 3 distinct characters, so the longest prefix with at most 3 distinct characters would always be all of it, therefore the answer is 1.

Example 3:

Input: s = "xxyz", k = 1

Output: 4

Explanation:

The optimal way is to change s[0] or s[1] to something other than characters in s, for example, to change s[0] to w.

Then s becomes "wxyz", which consists of 4 distinct characters, so as k is 1, it will divide into 4 partitions.

Constraints:

1 <= s.length <= 10⁴
s consists only of lowercase English letters.
1 <= k <= 26

Step 01

Brute Force Baseline

Problem summary: You are given a string s and an integer k. First, you are allowed to change at most one index in s to another lowercase English letter. After that, do the following partitioning operation until s is empty: Choose the longest prefix of s containing at most k distinct characters. Delete the prefix from s and increase the number of partitions by one. The remaining characters (if any) in s maintain their initial order. Return an integer denoting the maximum number of resulting partitions after the operations by optimally choosing at most one index to change.

Baseline thinking

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

Pattern signal: Dynamic Programming · Bit Manipulation

Example 1

"accca"
2

Example 2

"aabaab"
3

Example 3

"xxyz"
1

Core Insight

What unlocks the optimal approach

For each position, try to brute-force the replacements.
To speed up the brute-force solution, we can precompute the following (without changing any index) using prefix sums and binary search:<ul> <li><code>pref[i]</code>: The number of resulting partitions from the operations by performing the operations on <code>s[0:i]</code>.</li> <li><code>suff[i]</code>: The number of resulting partitions from the operations by performing the operations on <code>s[i:n - 1]</code>, where <code>n == s.length</code>.</li> <li><code>partition_start[i]</code>: The start index of the partition containing the <code>ith</code> index after performing the operations.</li> </ul>
Now, for a position <code>i</code>, we can try all possible <code>25</code> replacements: For a replacement, using prefix sums and binary search, we need to find the rightmost index, <code>r</code>, such that the number of distinct characters in the range <code>[partition_start[i], r]</code> is at most <code>k</code>. There are <code>2</code> cases:<ul> <li><code>r >= i</code>: the number of resulting partitions in this case is <code>1 + pref[partition_start[i] - 1] + suff[r + 1]</code>.</li> <li>Otherwise, we need to find the rightmost index <code>r2</code> such that the number of distinct characters in the range <code>[r:r2]</code> is at most <code>k</code>. The answer in this case is <code>2 + pref[partition_start[i] - 1] + suff[r2 + 1]</code></li> </ul>
The answer is the maximum among all replacements.

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

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 #3003: Maximize the Number of Partitions After Operations
class Solution {
    private Map<List<Integer>, Integer> f = new HashMap<>();
    private String s;
    private int k;

    public int maxPartitionsAfterOperations(String s, int k) {
        this.s = s;
        this.k = k;
        return dfs(0, 0, 1);
    }

    private int dfs(int i, int cur, int t) {
        if (i >= s.length()) {
            return 1;
        }
        var key = List.of(i, cur, t);
        if (f.containsKey(key)) {
            return f.get(key);
        }
        int v = 1 << (s.charAt(i) - 'a');
        int nxt = cur | v;
        int ans = Integer.bitCount(nxt) > k ? dfs(i + 1, v, t) + 1 : dfs(i + 1, nxt, t);
        if (t > 0) {
            for (int j = 0; j < 26; ++j) {
                nxt = cur | (1 << j);
                if (Integer.bitCount(nxt) > k) {
                    ans = Math.max(ans, dfs(i + 1, 1 << j, 0) + 1);
                } else {
                    ans = Math.max(ans, dfs(i + 1, nxt, 0));
                }
            }
        }
        f.put(key, ans);
        return ans;
    }
}

// Accepted solution for LeetCode #3003: Maximize the Number of Partitions After Operations
func maxPartitionsAfterOperations(s string, k int) int {
	n := len(s)
	type tuple struct{ i, cur, t int }
	f := map[tuple]int{}
	var dfs func(i, cur, t int) int
	dfs = func(i, cur, t int) int {
		if i >= n {
			return 1
		}
		key := tuple{i, cur, t}
		if v, ok := f[key]; ok {
			return v
		}
		v := 1 << (s[i] - 'a')
		nxt := cur | v
		var ans int
		if bits.OnesCount(uint(nxt)) > k {
			ans = dfs(i+1, v, t) + 1
		} else {
			ans = dfs(i+1, nxt, t)
		}
		if t > 0 {
			for j := 0; j < 26; j++ {
				nxt = cur | (1 << j)
				if bits.OnesCount(uint(nxt)) > k {
					ans = max(ans, dfs(i+1, 1<<j, 0)+1)
				} else {
					ans = max(ans, dfs(i+1, nxt, 0))
				}
			}
		}
		f[key] = ans
		return ans
	}
	return dfs(0, 0, 1)
}

# Accepted solution for LeetCode #3003: Maximize the Number of Partitions After Operations
class Solution:
    def maxPartitionsAfterOperations(self, s: str, k: int) -> int:
        @cache
        def dfs(i: int, cur: int, t: int) -> int:
            if i >= n:
                return 1
            v = 1 << (ord(s[i]) - ord("a"))
            nxt = cur | v
            if nxt.bit_count() > k:
                ans = dfs(i + 1, v, t) + 1
            else:
                ans = dfs(i + 1, nxt, t)
            if t:
                for j in range(26):
                    nxt = cur | (1 << j)
                    if nxt.bit_count() > k:
                        ans = max(ans, dfs(i + 1, 1 << j, 0) + 1)
                    else:
                        ans = max(ans, dfs(i + 1, nxt, 0))
            return ans

        n = len(s)
        return dfs(0, 0, 1)

// Accepted solution for LeetCode #3003: Maximize the Number of Partitions After Operations
// Rust example auto-generated from java reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (java):
// // Accepted solution for LeetCode #3003: Maximize the Number of Partitions After Operations
// class Solution {
//     private Map<List<Integer>, Integer> f = new HashMap<>();
//     private String s;
//     private int k;
// 
//     public int maxPartitionsAfterOperations(String s, int k) {
//         this.s = s;
//         this.k = k;
//         return dfs(0, 0, 1);
//     }
// 
//     private int dfs(int i, int cur, int t) {
//         if (i >= s.length()) {
//             return 1;
//         }
//         var key = List.of(i, cur, t);
//         if (f.containsKey(key)) {
//             return f.get(key);
//         }
//         int v = 1 << (s.charAt(i) - 'a');
//         int nxt = cur | v;
//         int ans = Integer.bitCount(nxt) > k ? dfs(i + 1, v, t) + 1 : dfs(i + 1, nxt, t);
//         if (t > 0) {
//             for (int j = 0; j < 26; ++j) {
//                 nxt = cur | (1 << j);
//                 if (Integer.bitCount(nxt) > k) {
//                     ans = Math.max(ans, dfs(i + 1, 1 << j, 0) + 1);
//                 } else {
//                     ans = Math.max(ans, dfs(i + 1, nxt, 0));
//                 }
//             }
//         }
//         f.put(key, ans);
//         return ans;
//     }
// }

// Accepted solution for LeetCode #3003: Maximize the Number of Partitions After Operations
function maxPartitionsAfterOperations(s: string, k: number): number {
    const n = s.length;
    const f: Map<bigint, number> = new Map();
    const dfs = (i: number, cur: number, t: number): number => {
        if (i >= n) {
            return 1;
        }
        const key = (BigInt(i) << 27n) | (BigInt(cur) << 1n) | BigInt(t);
        if (f.has(key)) {
            return f.get(key)!;
        }
        const v = 1 << (s.charCodeAt(i) - 97);
        let nxt = cur | v;
        let ans = 0;
        if (bitCount(nxt) > k) {
            ans = dfs(i + 1, v, t) + 1;
        } else {
            ans = dfs(i + 1, nxt, t);
        }
        if (t) {
            for (let j = 0; j < 26; ++j) {
                nxt = cur | (1 << j);
                if (bitCount(nxt) > k) {
                    ans = Math.max(ans, dfs(i + 1, 1 << j, 0) + 1);
                } else {
                    ans = Math.max(ans, dfs(i + 1, nxt, 0));
                }
            }
        }
        f.set(key, ans);
        return ans;
    };
    return dfs(0, 0, 1);
}

function bitCount(i: number): number {
    i = i - ((i >>> 1) & 0x55555555);
    i = (i & 0x33333333) + ((i >>> 2) & 0x33333333);
    i = (i + (i >>> 4)) & 0x0f0f0f0f;
    i = i + (i >>> 8);
    i = i + (i >>> 16);
    return i & 0x3f;
}

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 × |\Sigma| × k)

Space

O(n × |\Sigma| × k)

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.

Maximize the Number of Partitions After Operations

Problem Statement

Roadmap

Brute Force Baseline

Baseline thinking

Example 1

Example 2

Example 3

Related Problems

Core Insight

What unlocks the optimal approach

Algorithm Walkthrough

Iteration Checklist

Edge Cases

Full Annotated Code

Interactive Study Demo

Complexity Analysis

Approach Breakdown

Common Mistakes

State misses one required dimension