adventofcode VS aoc2021

Python day 17 solution (GitHub). Not the fewest lines of code, but used dataclasses and modular functions to try improve readability -- I got very confused with keeping track of indices of velocities vs coordinates in other people's posted solutions. I find dataclasses in Python can be a great way to be descriptive about the quantities you are iterating

Python day 15 solution (GitHub) using Networkx for graph algorithm and Numpy for building the bigger grid. Solutions to other days available in this repo

Python day 14 solution (GitHub). Tried splitting out the logic into individual functions to aid readability.

Python day 13 solutions (GitHub). Using Dataclasses to improve readability

Python solution (GitHub) using Gauss formula for part 2

Python solution using deque (GitHub). Much faster than my first attempt, lol

Python day 5 solutions using Bresenham's algorithm (Github)

Python solution day 4 OOP (GitHub). Probably not the fewest lines of code but uses dataclasses and OOP to manage the states of each bingo card

Python day 2 solution (GitHub)

Python day 1 solution (GitHub) using numpy's convolve function for sliding window calc

I've done every year in Haskell (2015 2016 2017 2018 2019 2020 2021), and so have several other people such as /u/glguy. I don't see if /u/mstksg has anything published for 2015 but they've done 2016 2017 2018 2019 2020 2021 along with some pretty good writeups, I recommend checking those out.

I ran my own benchmarks for a whole ton of code at https://github.com/ephemient/aoc2021 on both Linux x64 and macos x64, and ended up disabling Kotlin/Native because it was varying between 10x and 100x slower than Kotlin/JVM, eventually timing out on CI. The generated code may be reasonable thanks to LLVM, but the runtime certainly is not.

Took a while for me to get around to completing this in Kotlin, Python, and Rust as well, since I was working on optimizing other solutions, but I finally made it.

So I switched tacks and implemented this in Kotlin, where mutation is easier. First version was brute-force with duplicate state avoidance (checked before the state explosion at every input), which worked quickly enough for part 1, but ran into OOMs for part 2. Once I replaced the HashSet with a custom LruSet (and later a simpler CacheSet which simply overwrites on hash collisions) it ran part 2 in a few minutes. I then ported this into Haskell.

Haskell 1171/339

Kotlin, Python, and Rust solutions are the same solution but much uglier.

I'm doing the same cached recursion in Kotlin, Python, and Rust.

Haskell 621/603

Haskell 258/240

Haskell Kotlin 622/1185