What algorithm or data structure is used to solve Find Nearest Point That Has the Same X or Y Coordinate?

Find Nearest Point That Has the Same X or Y Coordinate uses the following concepts: Array. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Find Nearest Point That Has the Same X or Y Coordinate?

Clearly explain your thought process as you iterate through the points. Consider edge cases, such as when the current location is the same as a point. Optimize your solution after implementing the brute force method to show your understanding of efficiency.

What are common mistakes when solving Find Nearest Point That Has the Same X or Y Coordinate?

Not checking both x and y coordinates for validity. Forgetting to handle the case where no valid points exist.

Find Nearest Point That Has the Same X or Y Coordinate — LeetCode #1779 (Easy)

Tags: Array

Related patterns: Hash Map, Array

Brute Force approach

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

In this approach, we will check each point in the array to see if it shares the same x or y coordinate with our current location. If it does, we will calculate the Manhattan distance and keep track of the smallest distance found.

The time complexity is O(n) because we iterate through the list of points once. The space complexity is O(1) since we only use a few variables to store the minimum distance and index.

Step 1: Initialize a variable to track the minimum distance and the index of the closest point.
Step 2: Iterate through each point in the array.
Step 3: For each point, check if it shares the same x or y coordinate with the current location.
Step 4: If it does, calculate the Manhattan distance and update the minimum distance and index if this distance is smaller.
Step 5: After checking all points, return the index of the closest point or -1 if no valid points were found.

1. Start with x = 3, y = 4, points = [[1,2],[3,1],[2,4],[2,3],[4,4]]. 2. Initialize min_distance = inf, min_index = -1. 3. Check point [1,2]: No match, continue. 4. Check point [3,1]: Match (x), distance = 3, update min_distance = 3, min_index = 1. 5. Check point [2,4]: Match (y), distance = 1, update min_distance = 1, min_index = 2. 6. Check point [2,3]: No match, continue. Check point [4,4]: Match (y), distance = 1, but min_index remains 2. 7. Return min_index = 2.

Optimal Solution approach

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

This approach is similar to the brute force method but focuses on reducing unnecessary calculations. By maintaining a single pass through the points and directly calculating distances only for valid points, we ensure efficiency.

The time complexity remains O(n) as we still iterate through the points array once. The space complexity is O(1) because we only use a few variables to track the minimum distance and index.

Step 1: Initialize variables for minimum distance and index.
Step 2: Loop through the points array.
Step 3: For each point, check if it shares the x or y coordinate with the current location.
Step 4: If valid, calculate the Manhattan distance and update the minimum distance and index accordingly.
Step 5: Return the index of the closest valid point or -1 if none found.

Key Insights

Valid points are those that share either the x or y coordinate with the current location.
Manhattan distance is calculated as the sum of the absolute differences of the coordinates.

Common Mistakes

Not checking both x and y coordinates for validity.
Forgetting to handle the case where no valid points exist.

Interview Tips

Clearly explain your thought process as you iterate through the points.
Consider edge cases, such as when the current location is the same as a point.
Optimize your solution after implementing the brute force method to show your understanding of efficiency.

#1779

Find Nearest Point That Has the Same X or Y Coordinate

Easy

Array↗Hash Map↗Array↗

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

⚙️

Algorithm

5 steps

1Step 1: Initialize variables for minimum distance and index.
2Step 2: Loop through the points array.
3Step 3: For each point, check if it shares the x or y coordinate with the current location.
4Step 4: If valid, calculate the Manhattan distance and update the minimum distance and index accordingly.
5Step 5: Return the index of the closest valid point or -1 if none found.

solution.py10 lines

1def nearestValidPoint(x, y, points):
2    min_distance = float('inf')
3    min_index = -1
4    for i, (a, b) in enumerate(points):
5        if a == x or b == y:
6            distance = abs(x - a) + abs(y - b)
7            if distance < min_distance or (distance == min_distance and i < min_index):
8                min_distance = distance
9                min_index = i
10    return min_index

ℹ

Complexity note: The time complexity remains O(n) as we still iterate through the points array once. The space complexity is O(1) because we only use a few variables to track the minimum distance and index.

1Valid points are those that share either the x or y coordinate with the current location.
2Manhattan distance is calculated as the sum of the absolute differences of the coordinates.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.