Closest Pair
Detailed guide and Python implementation for the 'Closest Pair' problem.
1. Concept Overview
The 'Closest Pair' problem is a key challenge in the Searching & Sorting section.
This implementation focuses on easy-level logic in Python.
We prioritize technical accuracy and code readability in our provided solutions.
2. Real-World Applications
3. Visual Intuition
Visualizing the logic flow for Closest Pair.
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
Read the problem statement for Closest Pair carefully.
2. Formulate brute force
Draft a simple iterative solution.
3. Identify inefficiency
Look for redundant calculations.
4. Optimize search path
Use hashing or sorting to speed up the process.
5. Final Implementation
Clean up the code for production standards.
Problem Statement
Write a function closest_pair(arr) that takes an unsorted array of integers arr and returns a tuple of two integers (x, y) from the array that have the minimum absolute difference. The returned tuple must be sorted such that x <= y. If there are multiple pairs with the same minimum difference, return the one with the smaller x value (and if those are the same, the smaller y value).
- •2 <= len(arr) <= 10^5
- •-10^9 <= arr[i] <= 10^9
Examples
closest_pair([4, 9, 1, 32, 13])
(1, 4)
The difference between 1 and 4 is 3, which is the minimum difference possible between any two elements in the array.
closest_pair([12, 15, 17, 20, 25])
(15, 17)
The difference between 15 and 17 is 2, which is the minimum difference possible.
Need a Hint?
Edge Cases to Watch
- Empty input structures
- Single element inputs
- Large numerical bounds
Ready to Solve?
Open the problem in PyRun's browser-based Python editor. Your code runs fully offline — no server required.
Interview Insights & Variations
Complexity Analysis Breakdown
Why Time: Directly evaluates all possibilities.
Why Space: Uses standard local memory.
Why Time: Optimized paths reduce total operations.
Why Space: May trade memory for speed.
Optimized Solution Python Code
Optimized Solution Python Code
def closest_pair_opt(pts):
pts.sort()
def solve(P):
n = len(P)
if n <= 3: return closest_pair_brute(P)
mid = n // 2; dl = solve(P[:mid]); dr = solve(P[mid:])
d = min(dl, dr); mid_x = P[mid][0]
strip = [p for p in P if abs(p[0] - mid_x) < d]
strip.sort(key=lambda p: p[1])
for i in range(len(strip)):
for j in range(i + 1, len(strip)):
if (strip[j][1] - strip[i][1]) >= d: break
d = min(d, ((strip[i][0]-strip[j][0])**2 + (strip[i][1]-strip[j][1])**2)**0.5)
return d
return solve(pts)Brute Force Code (Spoiler Guarded)
Brute Force Code (Spoiler Guarded)
def closest_pair_brute(pts):
min_dist = float('inf')
for i in range(len(pts)):
for j in range(i + 1, len(pts)):
d = ((pts[i][0]-pts[j][0])**2 + (pts[i][1]-pts[j][1])**2)**0.5
min_dist = min(min_dist, d)
return min_distAlgorithm Pattern Checklist
When dealing with Searching & Sorting data patterns.
Core Prerequisites
Revision Key Notes
Common Mistakes & Pitfalls
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