Overview
This lecture introduces computational infeasibility, explaining why some algorithms are too complex for modern computers to execute efficiently and why understanding this concept is crucial for computer scientists.
What is Computational Infeasibility?
- Computational infeasibility occurs when an algorithm is too complex for computers to run in a reasonable time.
- Even as technology advances, some problem-solving ideas exceed the capabilities of current machines.
- Algorithms define step-by-step solutions, but some are not practical to implement due to their runtime demands.
Importance of Understanding Computational Infeasibility
- Recognizing infeasible algorithms helps computer scientists avoid unimplementable solutions.
- It is essential to think like a machine to predict how algorithms will perform in real-world settings.
- Understanding what makes an algorithm feasible but slow is vital for making practical design decisions.
Application to Computer Science
- Knowledge of computational infeasibility is important for computer organization and architecture.
- Evaluating an algorithm's feasibility ensures efficient programming and system design.
Key Terms & Definitions
- Algorithm — A sequence of steps to solve a problem.
- Computational Infeasibility — When an algorithm cannot be executed by computers within a practical timeframe.
Action Items / Next Steps
- Review examples of feasible and infeasible algorithms.
- Consider how computational infeasibility impacts your own programming and algorithm design.