[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

CLI Equivalent

Bash one-liner
# The same operation using shell tools (for CLI mastery practice)
cat data.txt | awk '{pattern}' | sort -k2 -nr | head -10

Walkthrough

Step-by-step with example input

Input
[sample input data]
Table 1. Trace
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

[1]

[Behavior]

All same values

[5, 5, 5]

[Behavior]

Maximum size

[1..10^6]

[Performance note]

Key Takeaways

  • [What pattern this teaches]

  • [When to use this approach]

  • [What to watch out for]

  • xref:[related-algorithm.adoc][Related algorithm]

  • https://[reference-url][External reference^]