What is the time complexity of Kids With the Greatest Number of Candies?

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

What data structures are used to solve Kids With the Greatest Number of Candies?

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

What are the key insights for solving Kids With the Greatest Number of Candies?

Insight 1: Finding the maximum number of candies among all kids is crucial. This allows us to compare each kid's candy count after receiving extra candies against this maximum. Insight 2: A simple linear scan through the `candies` array is sufficient to find the maximum and then build the result array. No sophisticated data structures are needed.

Which companies ask LeetCode 1431. Kids With the Greatest Number of Candies in interviews?

This problem is frequently asked at Infosys.

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Kids With the Greatest Number of Candies - LeetCode 1431 Solution

Kids With the Greatest Number of Candies - Complete Solution Guide

Kids With the Greatest Number of Candies is LeetCode problem 1431, 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

There are n kids with candies. You are given an integer array candies , where each candies[i] represents the number of candies the i th kid has, and an integer extraCandies , denoting the number of extra candies that you have. Return a boolean array result of length n , where result[i] is true if, after giving the i th kid all the extraCandies , they will have the greatest number of candies among all the kids , or false otherwise . Note that multiple kids can have the greatest number of candies.

Detailed Explanation

The problem asks to determine if each kid in a group will have the greatest number of candies after distributing extra candies. Given an array `candies` representing the initial candy count for each kid and an integer `extraCandies` representing the total number of extra candies, the goal is to return a boolean array `result`. Each element `result[i]` is `true` if kid `i`, after receiving `extraCandies`, would have the maximum number of candies among all kids, and `false` otherwise. Multiple kids can have the same maximum candy count.

Solution Approach

The provided solutions use a straightforward approach. First, they find the maximum number of candies among all kids. Then, for each kid, they add the `extraCandies` to their initial candy count and check if the resulting count is greater than or equal to the maximum candy count. The result is stored in a boolean array and returned.

Step-by-Step Algorithm

Step 1: Find the maximum number of candies (`maxCandies`) among all kids in the `candies` array.
Step 2: Create a boolean array `result` of the same size as `candies` to store the results.
Step 3: Iterate through the `candies` array. For each kid's candy count (`candyCount`):
Step 4: Add `extraCandies` to `candyCount`.
Step 5: Compare the sum (`candyCount + extraCandies`) with `maxCandies`. If the sum is greater than or equal to `maxCandies`, set the corresponding element in the `result` array to `true`; otherwise, set it to `false`.
Step 6: Return the `result` array.

Key Insights

Insight 1: Finding the maximum number of candies among all kids is crucial. This allows us to compare each kid's candy count after receiving extra candies against this maximum.
Insight 2: A simple linear scan through the `candies` array is sufficient to find the maximum and then build the result array. No sophisticated data structures are needed.
Insight 3: The problem is straightforward; the main challenge lies in efficiently finding the maximum candy count and then performing the comparison for each kid. Optimization focuses on minimizing the number of iterations.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(n)

Topics

This problem involves: Array.

Companies

Asked at: Infosys.

Kids With the Greatest Number of Candies | LeetCode 1431

Easy

Array

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

Solution Code

Available in:

import java.util.ArrayList;
import java.util.List;

class Solution {
    public List<Boolean> kidsWithCandies(int[] candies, int extraCandies) {
        int maxCandies = 0;
        for (int candy : candies) {
            maxCandies = Math.max(maxCandies, candy);
        }

        List<Boolean> result = new ArrayList<>();
        for (int candy : candies) {
            result.add(candy + extraCandies >= maxCandies);
        }

        return result;
    }
}

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Problem Info

Description

Problem 1431: Kids With the Greatest Number of Candies - Detailed explanation and solution approaches.

Topics

Array

Companies

Infosys

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:1431

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: Find the maximum number of candies (`maxCandies`) among all kids in the `candies` array.
2
Step 2: Create a boolean array `result` of the same size as `candies` to store the results.
3
Step 3: Iterate through the `candies` array. For each kid's candy count (`candyCount`):
4
Step 4: Add `extraCandies` to `candyCount`.
5
Step 5: Compare the sum (`candyCount + extraCandies`) with `maxCandies`. If the sum is greater than or equal to `maxCandies`, set the corresponding element in the `result` array to `true`; otherwise, set it to `false`.
6
Step 6: Return the `result` array.

💡

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(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The dominant operations are the two linear scans of the input array. The space used scales linearly with the number of kids as we create a boolean array to store the output.

📖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

Using a parallel stream (Java)

In Java, we could potentially use parallel streams to perform the check in Step 3-5 concurrently, which could lead to slight performance improvement on very large datasets.

Time: O(n) (potentially slightly faster with parallelization)Space: O(n)

Trade-offs: Parallelization introduces overhead. It is likely to be beneficial only for significantly large input sizes and may not outweigh the overhead for smaller datasets.

✨

Key Insights

1Insight 1: Finding the maximum number of candies among all kids is crucial. This allows us to compare each kid's candy count after receiving extra candies against this maximum.
2Insight 2: A simple linear scan through the `candies` array is sufficient to find the maximum and then build the result array. No sophisticated data structures are needed.
3Insight 3: The problem is straightforward; the main challenge lies in efficiently finding the maximum candy count and then performing the comparison for each kid. Optimization focuses on minimizing the number of iterations.

📚

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: All kids have the same number of candies. In this scenario, all kids will have the greatest number of candies after receiving extra candies, and the `result` array will contain all `true` values.
Edge case 2: Only one kid has the maximum number of candies. Only that kid will have `true` in the result array.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to add `extraCandies` to each kid's candy count before comparison. This leads to incorrect results.

✗Common mistake 2: Incorrectly handling the case where multiple kids have the maximum number of candies after receiving `extraCandies`.

✅

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: Determining which students, after receiving extra credit points, will have the highest score on an assignment or exam.

🏦Real-world scenario 2: Evaluating which employees, after receiving performance-based bonuses, will have the highest total compensation.

💡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

Even for easy problems, discuss time and space complexity. Interviewers want to see you think analytically.

⚠️ Watch Out

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

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: November 19, 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

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

@AlgoMasterJul 14

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

@InterviewProJul 7

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJun 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 "Kids With the Greatest Number of Candies"?

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.

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