Find Closest Number to Zero - Practice Coding Problems

Find Closest Number to Zero - Problem

Array Easy

Given an array of integers nums, your task is to find the number that has the smallest absolute distance to zero. Think of zero as the center point on a number line - you want to find which number in the array is closest to this center.

Here's the twist: if there's a tie (for example, both -3 and 3 are equally close to zero), you should return the larger positive number.

Example: In the array [-4, -2, 1, 4, 8], the number 1 is closest to zero with a distance of 1.

Tie Example: In the array [-1, 2, -1, 1], both -1 and 1 have the same distance (1) from zero, so we return 1 (the positive one).

Input & Output

example_1.py — Basic Case

$ Input: nums = [-4, -2, 1, 4, 8]

› Output: 1

💡 Note: The number 1 has the smallest absolute distance to zero (|1| = 1), making it the closest number.

example_2.py — Tie Case

$ Input: nums = [2, -1, 1]

› Output: 1

💡 Note: Both -1 and 1 have the same distance to zero (|−1| = |1| = 1). Since 1 > -1, we return the positive number 1.

example_3.py — All Negative

$ Input: nums = [-10, -5, -2, -1]

› Output: -1

💡 Note: Among all negative numbers, -1 has the smallest absolute distance to zero (|-1| = 1).

Constraints

1 ≤ nums.length ≤ 1000
-10⁵ ≤ nums[i] ≤ 10⁵
The array contains at least one element

Visualization

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Understanding the Visualization

Position Numbers

Place all numbers on a number line with zero at center

Measure Distances

Calculate absolute distance from each number to zero

Find Minimum

Identify the number(s) with smallest distance

Handle Ties

If multiple numbers have same distance, choose the positive one

Key Takeaway

🎯 Key Insight: Use absolute values to measure distance, but remember to prefer positive numbers in tie situations!

Asked in

G Google 15 a Amazon 12 ⊞ Microsoft 8 🍎 Apple 6

The optimal approach uses a single pass through the array, tracking the number with minimum absolute distance to zero. The key insight is handling tie-breaking by preferring positive numbers when distances are equal. Time complexity is O(n) and space complexity is O(1).

Common Approaches

Approach	Time	Space	Notes
✓ Brute Force (Compare All Distances)	O(n)	O(1)	Calculate distance to zero for each number and find the minimum

Brute Force (Compare All Distances) — Algorithm Steps

Initialize result with the first number
For each number, calculate abs(number - 0) = abs(number)
If distance is smaller, update result
If distance is equal but number is positive and result is negative, update result
Return the result

Visualization

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Step-by-Step Walkthrough

Initialize

Start with first number as closest

Compare Distance

For each number, compare its distance to zero

Handle Ties

If distances are equal, prefer positive numbers

Update Result

Keep track of the best candidate found so far

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

int findClosestNumber(int* nums, int numsSize) {
    int closest = nums[0];
    
    for (int i = 0; i < numsSize; i++) {
        // If this number is closer to zero
        if (abs(nums[i]) < abs(closest)) {
            closest = nums[i];
        }
        // If distances are equal, prefer positive number
        else if (abs(nums[i]) == abs(closest) && nums[i] > closest) {
            closest = nums[i];
        }
    }
    
    return closest;
}

int main() {
    char line[1000];
    fgets(line, sizeof(line), stdin);
    
    int nums[100];
    int count = 0;
    char* token = strtok(line, " ");
    while (token != NULL) {
        nums[count++] = atoi(token);
        token = strtok(NULL, " ");
    }
    
    printf("%d\n", findClosestNumber(nums, count));
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

We visit each element once to find the closest to zero

✓ Linear Growth

Space Complexity

O(1)

Only using a few variables to track the closest number

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Brute Force (Compare All Distances) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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