Algorithmic Efficency and Heuristic:

Problem: a general description of a task that can or cannot be solved algorithmically
Decision Problem: A problem with a yes or no answer
Organization Problem: a problem with a goal of finding the best answer
Instance: a problem with a specific input
Efficiency: amount of computing needed to solve a problem
Polynomial Efficiency (Good): more work takes a proportional amount of time (1 job is +2 time)
Exponential Efficiency (Bad): more work takes an exponential amount more time (1 job is 2x time)
Heuristic Approach: When optimal solutions are inefficient, look for a possibly optimal solution that is more efficient
Decidable Problem: A decision problem that has a clear solution that will always make a correct output
Undecidable Problem: A decision problem with no solution that is not guaranteed to produce the correct output
Heuristic solutions are when we sacrifice optimal solutions to improve efficiency and ease of programming
Used when normal methods would take forever.

Lesson Plan:

Me and Max work on Algorithmic Efficiency and Alan and Evan work on the Undecidable problems. Max goes through the main topic and big O notation based on his notes on Khan Academy and College board. I use the college board example, to explain what is happening. We will establish a purpose and go about the lesson building surrounding that purpose.