Longer Contiguous Segments of Ones than Zeros - Problem

String Easy

Given a binary string s, return true if the longest contiguous segment of 1's is strictly longer than the longest contiguous segment of 0's in s, or return false otherwise.

For example, in s = "110100010" the longest continuous segment of 1s has length 2, and the longest continuous segment of 0s has length 3.

Note that if there are no 0's, then the longest continuous segment of 0's is considered to have a length 0. The same applies if there is no 1's.

Input & Output

Example 1 — Mixed Segments

$ Input: s = "110100010"

› Output: false

💡 Note: Longest segment of 1s has length 2 (positions 0-1), longest segment of 0s has length 3 (positions 4-6). Since 2 ≤ 3, return false.

Example 2 — 1s Win

$ Input: s = "1111000"

› Output: true

💡 Note: Longest segment of 1s has length 4 (positions 0-3), longest segment of 0s has length 3 (positions 4-6). Since 4 > 3, return true.

Example 3 — All Ones

$ Input: s = "111"

› Output: true

💡 Note: Only 1s present with length 3, no 0s (length 0). Since 3 > 0, return true.

Constraints

1 ≤ s.length ≤ 1000
s consists only of '0' and '1'

Visualization

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Asked in

G Google 15 a Amazon 12 f Meta 8

The key insight is to track the maximum length of contiguous segments for both 0s and 1s in a single pass through the string. The optimal approach uses single-pass tracking with segment change detection. Time: O(n), Space: O(1)

Common Approaches

✓ Brute Force - Find All Segments

⏱️ Time: O(n) Space: O(n)

Iterate through the string multiple times to identify all contiguous segments of 0s and 1s separately, storing their lengths, then find the maximum length for each type.

Single Pass - Track Maximum Lengths

⏱️ Time: O(n) Space: O(1)

Iterate through the string once, keeping track of current segment length and updating maximum lengths for both 0s and 1s as we encounter segment changes.

Brute Force - Find All Segments — Algorithm Steps

Step 1: Find all contiguous segments of 1s and store their lengths
Step 2: Find all contiguous segments of 0s and store their lengths
Step 3: Compare the maximum lengths of both types

Visualization

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Step-by-Step Walkthrough

Find 1s Segments

Scan for all contiguous 1s: [2, 1]

Find 0s Segments

Scan for all contiguous 0s: [1, 3, 2]

Compare Max

max(1s)=2, max(0s)=3, return false

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdbool.h>

bool solution(char* s) {
    int onesSegments[1000];
    int zerosSegments[1000];
    int onesCount = 0, zerosCount = 0;
    
    // Find all segments of 1s
    int i = 0;
    int len = strlen(s);
    while (i < len) {
        if (s[i] == '1') {
            int count = 0;
            while (i < len && s[i] == '1') {
                count++;
                i++;
            }
            onesSegments[onesCount++] = count;
        } else {
            i++;
        }
    }
    
    // Find all segments of 0s
    i = 0;
    while (i < len) {
        if (s[i] == '0') {
            int count = 0;
            while (i < len && s[i] == '0') {
                count++;
                i++;
            }
            zerosSegments[zerosCount++] = count;
        } else {
            i++;
        }
    }
    
    int maxOnes = 0;
    for (int j = 0; j < onesCount; j++) {
        if (onesSegments[j] > maxOnes) {
            maxOnes = onesSegments[j];
        }
    }
    
    int maxZeros = 0;
    for (int j = 0; j < zerosCount; j++) {
        if (zerosSegments[j] > maxZeros) {
            maxZeros = zerosSegments[j];
        }
    }
    
    return maxOnes > maxZeros;
}

int main() {
    char s[1001];
    fgets(s, sizeof(s), stdin);
    s[strcspn(s, "\n")] = 0;
    bool result = solution(s);
    printf(result ? "true\n" : "false\n");
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Two passes through the string of length n

✓ Linear Growth

Space Complexity

O(n)

Arrays to store all segment lengths in worst case

✓ Linear Space

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Medium Frequency

~8 min Avg. Time

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Longer Contiguous Segments of Ones than Zeros - Problem

Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Brute Force - Find All Segments — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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