The Sorting Hat

Jed Rembold

April 17, 2026

Happy Penultimate Friday!!

Given this situation, I am going to offer some exam corrections
If you lost points on a non-extra credit problem, you can earn up to 50% of the lost points back by:
- Creating a Trilogy Portfolio: Collecting learning objectives, identifying past examples in in-class work and in your work, synthesizing why you didn’t make the connections and how to improve going forwards
- Creating a new problem and solution: New problem should address at least 80% of your identifying learning objectives, and your solution should be correct. Must also write a “Design Rationale” paragraph about why you made the choices you did in writing your solution.
Full guide can be found here
Must be completed for each problem you want points back on
Due midnight of May 1st. Past that point, you will not be able to submit.
100% optional, and you can not earn over a 100% by doing corrections

How does selection sort operate?
How can we get an idea for how an algorithm will scale just by looking at the code?
What is Big-O notation?
Can we identify examples of Big-O notation?

I am distributing small stacks of playing cards to each of your groups
Designate:
- The processor: the one pointing at the current card that is being looked at
- The memory: the one whose left and right hands will mark the special positions
- The counter: the one tallying how many cards have been looked at
Shuffle the cards and deal out 5 in a row
- Sort them according to selection sort, following your roles
- How many cards did you look at? Does that agree with what you’d have thought?
Shuffle all the cards back together and deal them all out.
- Sort again. How many cards were looked at this time? Does this agree any better?

We are going to cycle through some real-world scenarios here
For each, you’ll have about 2 minutes to discuss with your group what Big-O scaling you think the process would involve

Grabbing the largest wrench in a toolbox organized with molded slots for each tool.

Retrieving a part from the bin labeled “Part #105” in a large, organized warehouse.

Creating a seating chart that ensures that no “mortal enemies” are seated together at the same table.

You have written a lot of code over the course of the semester!
The following slide will have a handful of target “bounties”
Your task is to fine examples of each bounty in code that you or someone in your group has written over the course of the semester.
Be prepared to defend your choices to another group!

Find a snippet that looks like a single \(\mathcal{O}(N)\) loop but is actually \(\mathcal{O}(N^2)\) because of a built-in Python function.
Find an \(\mathcal{O}(1)\) operation
Find an \(\mathcal{O}(N^2)\) operation that does not use two nested for loops
Find an \(\mathcal{O}(N)\) operation that finishes in \(\mathcal{O}(1)\) under special conditions
Find the heaviest code you wrote this semester. How does it scale?

This is a coding problem!
Linear search is usually \(\mathcal{O}(N)\) in the worst case situation.
Design a searching algorithm that runs in \(\mathcal{O}(N^3)\)
- It must still work! That is, it must still return a correct answer.