What is the time complexity of Max Consecutive Ones?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Max Consecutive Ones?

This problem uses Array. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Max Consecutive Ones?

Insight 1: The core idea is to maintain a 'current_count' of consecutive 1s and a 'max_count' to store the largest consecutive sequence seen so far. Insight 2: The algorithm efficiently tracks consecutive 1s by incrementing 'current_count' when a 1 is encountered and resetting it to 0 when a 0 is encountered.

Which companies ask LeetCode 485. Max Consecutive Ones in interviews?

This problem is frequently asked at Accenture, Deloitte, Yandex.

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Max Consecutive Ones - LeetCode 485 Solution

Q: How to solve Max Consecutive Ones?

The provided solution utilizes a simple iterative approach to find the maximum consecutive ones. It maintains two variables: `max_count` and `current_count`. The algorithm iterates through the array, incrementing `current_count` when it encounters a 1. When it encounters a 0, it updates `max_count` with the maximum of `max_count` and `current_count`, and then resets `current_count` to 0. Finally, after iterating through the entire array, it updates `max_count` one last time to account for the case where the array ends with a sequence of 1s.

Max Consecutive Ones - Complete Solution Guide

Max Consecutive Ones is LeetCode problem 485, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Given a binary array nums , return the maximum number of consecutive 1 's in the array . Example 1: Input: nums = [1,1,0,1,1,1] Output: 3 Explanation: The first two digits or the last three digits are consecutive 1s. The maximum number of consecutive 1s is 3. Example 2: Input: nums = [1,0,1,1,0,1] Output: 2 Constraints: 1 <= nums.length <= 10 5 nums[i] is either 0 or 1 .

Detailed Explanation

The problem asks us to find the maximum number of consecutive 1s in a given binary array (an array containing only 0s and 1s). We need to iterate through the array and keep track of the current consecutive count of 1s. Whenever we encounter a 0, we compare the current count with the maximum count found so far and update the maximum count if necessary. After the loop, we also need to compare the current count with the maximum count, as the array might end with a sequence of 1s.

Solution Approach

The provided solution utilizes a simple iterative approach to find the maximum consecutive ones. It maintains two variables: `max_count` and `current_count`. The algorithm iterates through the array, incrementing `current_count` when it encounters a 1. When it encounters a 0, it updates `max_count` with the maximum of `max_count` and `current_count`, and then resets `current_count` to 0. Finally, after iterating through the entire array, it updates `max_count` one last time to account for the case where the array ends with a sequence of 1s.

Step-by-Step Algorithm

Step 1: Initialize `max_count` and `current_count` to 0.
Step 2: Iterate through the input array `nums`.
Step 3: If the current element is 1, increment `current_count`.
Step 4: If the current element is 0, update `max_count` with the maximum of `max_count` and `current_count`, then reset `current_count` to 0.
Step 5: After the loop, update `max_count` one last time with the maximum of `max_count` and `current_count` to handle cases where the array ends with consecutive 1s.
Step 6: Return `max_count`.

Key Insights

Insight 1: The core idea is to maintain a 'current_count' of consecutive 1s and a 'max_count' to store the largest consecutive sequence seen so far.
Insight 2: The algorithm efficiently tracks consecutive 1s by incrementing 'current_count' when a 1 is encountered and resetting it to 0 when a 0 is encountered.
Insight 3: It's crucial to update 'max_count' one last time after the loop in case the array ends with a sequence of 1s, which would not have been captured otherwise.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(1)

Topics

This problem involves: Array.

Companies

Asked at: Accenture, Deloitte, Yandex.

Max Consecutive Ones | LeetCode 485

Easy

Array

Time: O(n)Space: O(1)

Solution Code

Available in:

class Solution {
    public int findMaxConsecutiveOnes(int[] nums) {
        int maxCount = 0;
        int currentCount = 0;
        for (int num : nums) {
            if (num == 1) {
                currentCount++;
            } else {
                maxCount = Math.max(maxCount, currentCount);
                currentCount = 0;
            }
        }
        maxCount = Math.max(maxCount, currentCount);
        return maxCount;
    }
}

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Problem Info

Description

Problem 485: Max Consecutive Ones - Detailed explanation and solution approaches.

Topics

Array

Companies

AccentureDeloitteYandex

Stats

Difficulty:Easy

Languages:4

Companies:3

Problem #:485

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

6 steps

1
Step 1: Initialize `max_count` and `current_count` to 0.
2
Step 2: Iterate through the input array `nums`.
3
Step 3: If the current element is 1, increment `current_count`.
4
Step 4: If the current element is 0, update `max_count` with the maximum of `max_count` and `current_count`, then reset `current_count` to 0.
5
Step 5: After the loop, update `max_count` one last time with the maximum of `max_count` and `current_count` to handle cases where the array ends with consecutive 1s.
6
Step 6: Return `max_count`.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The time complexity is linear because each element in the array is visited exactly once. The space complexity is constant as the amount of memory used by the algorithm does not depend on the size of the input array.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

None (This is the most efficient approach)

There isn't a significantly better approach in terms of time complexity. Other approaches might involve splitting the string or using more complex data structures, but they wouldn't offer any performance advantages and would likely be less efficient.

Time: O(n)Space: O(1)

Trade-offs: No significant tradeoffs compared to the provided solution. This approach is the simplest and most efficient.

✨

Key Insights

1Insight 1: The core idea is to maintain a 'current_count' of consecutive 1s and a 'max_count' to store the largest consecutive sequence seen so far.
2Insight 2: The algorithm efficiently tracks consecutive 1s by incrementing 'current_count' when a 1 is encountered and resetting it to 0 when a 0 is encountered.
3Insight 3: It's crucial to update 'max_count' one last time after the loop in case the array ends with a sequence of 1s, which would not have been captured otherwise.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Edge Cases

Edge case 1: Empty array. The provided code handles an empty array correctly because the loop will not execute, and both `max_count` and `current_count` will remain 0, which is the correct result.
Edge case 2: Array with all 0s. The `current_count` will always be 0, so `max_count` will remain 0. The final `max_count` update after the loop will also result in 0.
Edge case 3: Array with all 1s. `current_count` will increment to `n`, where n is the length of the array. The final max_count update will result in max_count being equal to n.
Edge case 4: Array ends with a sequence of 1s. The algorithm correctly handles this case by comparing `max_count` and `current_count` one last time after the loop.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to update `max_count` after the loop. If the input array ends with a sequence of 1s, the final `current_count` won't be compared to `max_count` unless it's explicitly done after the loop.

✗Common mistake 2: Incorrectly resetting `current_count`. Ensure `current_count` is reset to *0* when a 0 is encountered. Resetting it to 1 would lead to incorrect results.

✗Common mistake 3: Confusing `max_count` and `current_count`. It's important to clearly distinguish between the overall maximum count seen so far and the count of the current consecutive sequence.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Analyzing network traffic logs to identify periods of high activity or connection stability. Consecutive 1s could represent successful connections or periods of uninterrupted service.

🏦Real-world scenario 2: Detecting patterns in DNA sequences. Consecutive sequences of a particular nucleotide (A, T, C, or G) can indicate specific genetic markers or regions of interest. Think of 1s and 0s as representing the presence or absence of a particular gene.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Don't rush. Explain your thought process clearly. Easy problems are often about communication.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

👨‍💻 From Experience

This is one of the first problems I solved when learning Array. It's a great foundation for more complex problems.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: October 4, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 14

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 7

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJun 30

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

What should I learn from easy problems?

Easy problems introduce core patterns that appear in harder problems. Master basic operations (iteration, conditionals, simple data structures), recognize common patterns (counting, searching, basic transformations), and practice explaining your thought process clearly.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Max Consecutive Ones"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether standard techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

🤖 Need help with this problem?