Algorithm Analysis

Learning Objectives

• Evaluate algorithm efficiency using Big O notation.
• Compare alternative approaches logically.
• Explain time/space trade-offs clearly.

Core Concepts

• Time Complexity: growth of execution steps.
• Space Complexity: extra memory needed.
• Best/Average/Worst case behavior.

Quick Examples

• Array index access: O(1)
• Linear search: O(n)
• Binary search (sorted): O(log n)
• Nested full scan: O(n^2)

Analysis Workflow

1. Count dominant operations.
2. Express growth with Big O.
3. Compare candidates for same problem.
4. Validate with real input sizes.

Summary

Good analysis prevents expensive design mistakes early.