LeetCode #3163 — MEDIUM

String Compression III

Move from brute-force thinking to an efficient approach using core interview patterns strategy.

Solve on LeetCode

The Problem

Problem Statement

Given a string word, compress it using the following algorithm:

Begin with an empty string comp. While word is not empty, use the following operation:
- Remove a maximum length prefix of word made of a single character c repeating at most 9 times.
- Append the length of the prefix followed by c to comp.

Return the string comp.

Example 1:

Input: word = "abcde"

Output: "1a1b1c1d1e"

Explanation:

Initially, comp = "". Apply the operation 5 times, choosing "a", "b", "c", "d", and "e" as the prefix in each operation.

For each prefix, append "1" followed by the character to comp.

Example 2:

Input: word = "aaaaaaaaaaaaaabb"

Output: "9a5a2b"

Explanation:

Initially, comp = "". Apply the operation 3 times, choosing "aaaaaaaaa", "aaaaa", and "bb" as the prefix in each operation.

For prefix "aaaaaaaaa", append "9" followed by "a" to comp.
For prefix "aaaaa", append "5" followed by "a" to comp.
For prefix "bb", append "2" followed by "b" to comp.

Constraints:

1 <= word.length <= 2 * 10⁵
word consists only of lowercase English letters.

Step 01

Brute Force Baseline

Problem summary: Given a string word, compress it using the following algorithm: Begin with an empty string comp. While word is not empty, use the following operation: Remove a maximum length prefix of word made of a single character c repeating at most 9 times. Append the length of the prefix followed by c to comp. Return the string comp.

Baseline thinking

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

Pattern signal: General problem-solving

Example 1

"abcde"

Example 2

"aaaaaaaaaaaaaabb"

Core Insight

What unlocks the optimal approach

Each time, just cut the same character in prefix up to at max 9 times. It’s always better to cut a bigger prefix.

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 #3163: String Compression III
class Solution {
    public String compressedString(String word) {
        StringBuilder ans = new StringBuilder();
        int n = word.length();
        for (int i = 0; i < n;) {
            int j = i + 1;
            while (j < n && word.charAt(j) == word.charAt(i)) {
                ++j;
            }
            int k = j - i;
            while (k > 0) {
                int x = Math.min(9, k);
                ans.append(x).append(word.charAt(i));
                k -= x;
            }
            i = j;
        }
        return ans.toString();
    }
}

// Accepted solution for LeetCode #3163: String Compression III
func compressedString(word string) string {
	ans := []byte{}
	n := len(word)
	for i := 0; i < n; {
		j := i + 1
		for j < n && word[j] == word[i] {
			j++
		}
		k := j - i
		for k > 0 {
			x := min(9, k)
			ans = append(ans, byte('0'+x))
			ans = append(ans, word[i])
			k -= x
		}
		i = j
	}
	return string(ans)
}

# Accepted solution for LeetCode #3163: String Compression III
class Solution:
    def compressedString(self, word: str) -> str:
        g = groupby(word)
        ans = []
        for c, v in g:
            k = len(list(v))
            while k:
                x = min(9, k)
                ans.append(str(x) + c)
                k -= x
        return "".join(ans)

// Accepted solution for LeetCode #3163: String Compression III
// 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 #3163: String Compression III
// class Solution {
//     public String compressedString(String word) {
//         StringBuilder ans = new StringBuilder();
//         int n = word.length();
//         for (int i = 0; i < n;) {
//             int j = i + 1;
//             while (j < n && word.charAt(j) == word.charAt(i)) {
//                 ++j;
//             }
//             int k = j - i;
//             while (k > 0) {
//                 int x = Math.min(9, k);
//                 ans.append(x).append(word.charAt(i));
//                 k -= x;
//             }
//             i = j;
//         }
//         return ans.toString();
//     }
// }

// Accepted solution for LeetCode #3163: String Compression III
function compressedString(word: string): string {
    const ans: string[] = [];
    const n = word.length;
    for (let i = 0; i < n; ) {
        let j = i + 1;
        while (j < n && word[j] === word[i]) {
            ++j;
        }
        let k = j - i;
        while (k) {
            const x = Math.min(k, 9);
            ans.push(x + word[i]);
            k -= x;
        }
        i = j;
    }
    return ans.join('');
}

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)

Space

O(n)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

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.