Complex Algorithms and Optimization · Algorithms and Programming
Algorithmic Efficiency and Big O
Understanding how to mathematically evaluate the performance of code as input size grows. Students compare linear, logarithmic, and quadratic growth patterns.
Key Questions
- 1How do we determine which algorithm is best when resources are limited?
- 2What are the real-world consequences of choosing an O(n squared) algorithm over an O(n log n) one?
- 3How does hardware evolution change our perception of algorithmic efficiency?
Common Core State Standards
CSTA: 3B-AP-11
Grade: 12th Grade
Subject: Computer Science
Unit: Complex Algorithms and Optimization
Period: Algorithms and Programming
Suggested Methodologies
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