Today we move on to memoization and dynamic programming! This is a widely applicable technique that also lets us practice algorithm design and analysis, designing recursive solutions, and designing iterative solutions. Woo-hoo! 🙂
- Here are today’s notes (part 1 on Memoization and DP using the change example).
- For Monday, read Section 6.1.
- I will try to post a quiz, but the UBC Survey system is crashing for me. If I cannot post it by noon on Saturday, I won’t, but please work the problems anyway!
Here is the quiz:
- The recursive solution to the weighted interval scheduling problem has to make one key decision among two or more choices by trying all the choices and picking the best. What is the decision?
- Which among all the previous events will go before this event
- Whether or not to include this event in the solution
- Whether the next event’s start time is before this event’s finish time (i.e., they conflict)
- How do we figure out that there are only n+1 total possible sub-problems in the memoized solution?
- Because the only legal arguments to the algorithm are 0 through n.
- Because of the algorithm’s “for” loop going from 0 up to n.
- Because the algorithm runs in O(n) time.
- The recursive solution to the weighted interval scheduling problem has to make one key decision among two or more choices by trying all the choices and picking the best. What is the decision?
- We’re working on grading the exam and will be done soon. However, scanning/handback is likely to take until roughly class time on Wednesday.
- There will be no quiz in next week’s class. Sorry! Too busy with exam prep/marking to get one together 🙁