[Language/Tool]: [Algorithm or Concept]
Problem Statement
Constraints
-
[Input constraints]
-
[Performance requirements]
-
[Edge cases to handle]
Approach
Complexity Analysis
| Metric | Value |
|---|---|
Time complexity |
\$O(n)\$ / \$O(n \log n)\$ / \$O(n^2)\$ |
Space complexity |
\$O(1)\$ / \$O(n)\$ |
Best case |
[When it performs best] |
Worst case |
[When it performs worst] |
Implementation
Version 1: [Naive / Brute Force]
# Naive implementation — establishes correctness
def solution_v1(data):
"""
Brief description.
Args:
data: input description
Returns:
output description
"""
pass
Test
# Verify correctness
assert solution_v1([1, 2, 3]) == expected
assert solution_v1([]) == edge_case
Version 2: [Optimized]
# Optimized implementation — explain what changed and why
def solution_v2(data):
pass
Version 3: [Idiomatic / Production]
# Production version — error handling, typing, docstrings
from typing import List
def solution_final(data: List[int]) -> int:
"""Production implementation with full type hints."""
if not data:
raise ValueError("Input cannot be empty")
pass
Walkthrough
Step-by-step with example input
Input
[sample input data]
| Step | State | Operation | Result |
|---|---|---|---|
1 |
[Initial state] |
[What happens] |
[New state] |
2 |
[State] |
[What happens] |
[New state] |
Output
[expected output]
Edge Cases
| Case | Input | Expected |
|---|---|---|
Empty input |
|
[Behavior] |
Single element |
|
[Behavior] |
All same values |
|
[Behavior] |
Maximum size |
|
[Performance note] |
Key Takeaways
-
[What pattern this teaches]
-
[When to use this approach]
-
[What to watch out for]
Related
-
xref:[related-algorithm.adoc][Related algorithm]
-
https://[reference-url][External reference^]