By Eric Burden | December 17, 2022

It’s that time of year again! Just like last year, I’ll be posting my solutions to the Advent of Code puzzles. This year, I’ll be solving the puzzles in Rust. I’ll post my solutions and code to GitHub as well. After finishing last year (and 2015-2019) in Julia, I needed to spend some time with Rust again! If you haven’t given AoC a try, I encourage you to do so along with me!

# Day 16 - Proboscidea Volcanium

Find the problem description HERE.

## The Input - Got Me Under Pressure

Today’s input is a *lot* like yesterday’s, at least in initial presentation.
Each line has three pieces of useful information, with a bunch of verbiage
that needs to be ignored surrounding those three pieces. We’ll want the current
valve’s label, the pressure that can be released by that valve, and the other
valves that can be reached by tunnel. The big difference here is that the
algorithms for solving the puzzle really benefit from cutting down the input
from the information as presented to a much more condensed graph structure where
only the paths between the valves that actually release pressure are included.
That part takes quite a bit of work, but it makes it worth it when our solution
runs in milliseconds instead of hours.

```
use std::collections::HashMap;
use itertools::Itertools;
/// Represents a line from the input, parsed to pull out the relevant values.
/// Using this intermediate data structure because the _actual_ input gets
/// a lot of massaging before it's ready for use.
#[derive(Debug)]
struct ParsedInput<'a> {
label: &'a str,
flow: u32,
leads_to: Vec<&'a str>,
}
impl<'a> ParsedInput<'a> {
fn new(label: &'a str, flow: u32, leads_to: Vec<&'a str>) -> Self {
Self {
label,
flow,
leads_to,
}
}
}
/// Conversion for a tuple containing the parsed input values to a `ParsedInput`.
/// Really, `ParsedInput` is just a tuple with appropriate names.
impl<'a> From<(&'a str, u32, Vec<&'a str>)> for ParsedInput<'a> {
fn from(value: (&'a str, u32, Vec<&'a str>)) -> Self {
let (label, flow, leads_to) = value;
ParsedInput::new(label, flow, leads_to)
}
}
/// Module wrapping the parsing functions for parsing the input.
mod parser {
use super::*;
use anyhow::{anyhow, Result};
use nom::{
branch::alt,
bytes::complete::tag,
character::complete::{alpha1, newline, u32},
combinator::{map, verify},
multi::separated_list1,
sequence::{delimited, preceded, tuple},
Finish, IResult,
};
/// Parses a valve label, which is always a string of two capital letters.
fn label(s: &str) -> IResult<&str, &str> {
let ver_fn = |s: &str| s.len() == 2 && s.chars().all(|c| c.is_ascii_uppercase());
verify(alpha1, ver_fn)(s)
}
/// Nom parser for "Valve AA" -> "AA"
fn valve(s: &str) -> IResult<&str, &str> {
preceded(tag("Valve "), label)(s)
}
/// Parses the flow rate of a valve
fn flow(s: &str) -> IResult<&str, u32> {
delimited(tag(" has flow rate="), u32, tag(";"))(s)
}
/// Parses the list of valves that are listed at the end of each input line.
/// Note that the prefixes use proper subject-verb agreement.
fn leads_to(s: &str) -> IResult<&str, Vec<&str>> {
let prefix1 = tag(" tunnels lead to valves ");
let prefix2 = tag(" tunnel leads to valve ");
preceded(alt((prefix1, prefix2)), separated_list1(tag(", "), label))(s)
}
/// Parses a line of the input into a `ParsedInput`
fn valve_map_entry(s: &str) -> IResult<&str, ParsedInput> {
map(tuple((valve, flow, leads_to)), ParsedInput::from)(s)
}
/// Parses multiple lines from the input into a list of `ParsedInput`s
fn valve_map_entries(s: &str) -> IResult<&str, Vec<ParsedInput>> {
separated_list1(newline, valve_map_entry)(s)
}
/// Main parsing function, attemtps to read the input file as a string into
/// a list of `ParsedInput`s.
pub fn parse(s: &str) -> Result<Vec<ParsedInput>> {
let (_, result) = valve_map_entries(s).finish().map_err(|e| anyhow!("{e}"))?;
Ok(result)
}
}
/// Represents a valve. Includes fields for its ID and flow rate. The ID for
/// a valve is a u64 with a single bit set. This makes keeping up with which
/// valves have been opened as easy as bitwise -or- between multiple valves.
#[derive(Debug, Copy, Clone)]
struct Valve {
id: u64,
flow: u32,
}
impl Valve {
fn new(id: u64, flow: u32) -> Self {
Valve { id, flow }
}
}
/// Represents a graph indicating the connections between valves. Edges are a list
/// of lists, where each list contains a pair of (edge index, distance to edge). For
/// example, if edges[0] == [(1, 2), (5, 3)], that indicates that from the valve
/// at index [0], the valve at index [1] can be reached in 2 steps and the valve at
/// index [5] can be reached in 3 steps.
/// The nodes are just the list of `Valve`s at an index that corresponds to an index
/// in `edges`. For example, if nodes[0] is valve "AA", then edges[0] indicates the
/// valves that can be reached from valve "AA".
#[derive(Debug)]
pub struct ValveMap {
pub edges: Vec<Vec<(usize, u32)>>,
pub nodes: Vec<Valve>,
}
impl ValveMap {
fn new(edges: Vec<Vec<(usize, u32)>>, nodes: Vec<Valve>) -> Self {
Self { edges, nodes }
}
}
/// Convert a list of parsed input lines into a ValveMap
impl From<Vec<ParsedInput<'_>>> for ValveMap {
fn from(value: Vec<ParsedInput>) -> Self {
// The first part is the easy part, the nodes (valves). We set each ID
// sequentially and extract the flow rate from the parsed input line.
let mut nodes = Vec::new();
for (idx, entry) in value.iter().enumerate() {
let id = 2u64.pow(idx as u32);
let flow = entry.flow;
let valve = Valve::new(id, flow);
nodes.push(valve);
}
// Given the fact that valves and their corresponding paths out are stored
// and referenced by index in the ValveMap and they are given by label
// (like "AA") by the input lines, it's handy to be able to get the appropriate
// index for a valve by it's label.
let label_idx_map = value
.iter()
.enumerate()
.map(|(idx, entry)| (entry.label, idx))
.collect::<HashMap<_, _>>();
// The first step in preparing the `ValveMap` is to figure out the minimum
// distance from each valve to each other valve. This is an implementation of
// a thing I just learned about today called the 'Floyd-Warshall' algorithm.
// The TLDR is that we're creating a 2D vector where each row represents a
// valve to travel from, each column represents a valve to travel to, and the
// value at that grid intersection is the cost. To begin, we set all the travel
// distances to (essentially) infinity, then for each parsed input line telling
// us which valves are directly connected, set those distances to 1, since we
// know it takes 1 minute to travel each direct connection
let mut dist_matrix = vec![vec![u32::MAX; value.len()]; value.len()];
for (idx, entry) in value.iter().enumerate() {
dist_matrix[idx][idx] = 0;
for neighbor in entry.leads_to.iter() {
let neighbor_idx = label_idx_map[neighbor];
dist_matrix[idx][neighbor_idx] = 1;
}
}
// With the direct connections in place, we check over every 3-wise
// permutation of grid indices. Then we compare the distance from valve [i] to
// valve [j] directly and the distance from valves [i] > [k] > [j]. If the path
// with the detour is shorter than the direct path, update the distance
// from [i] > [j] with the shorter distance.
for permutation in (0..value.len()).permutations(3) {
let (k, i, j) = (permutation[0], permutation[1], permutation[2]);
let detour_dist = dist_matrix[i][k].saturating_add(dist_matrix[k][j]);
if detour_dist < dist_matrix[i][j] {
dist_matrix[i][j] = detour_dist;
}
}
// Now that we know the shortest distance between all pairs of valves, we
// build out our list of edges. We'll only include edges to valves that
// actually release some amount of pressure (more than 0).
let mut edges = Vec::new();
for (start, valve) in nodes.iter().enumerate() {
// For each possible destination valve, decide whether it should be
// added to the list of edges from the [start] node.
let mut edges_from = Vec::new();
for (end, end_valve) in nodes.iter().enumerate() {
// Don't include edges where the destination is the same
// as the start or the valve's flow rate is zero.
if start == end || end_valve.flow == 0 {
continue;
}
// Otherwise, add the edge as the destination index and the
// minimum distance from [start] to [end].
edges_from.push((end, dist_matrix[start][end]));
}
edges.push(edges_from);
}
ValveMap::new(edges, nodes)
}
}
const INPUT: &str = include_str!("../../input/16/input.txt");
fn read() -> ValveMap {
let entries = parser::parse(INPUT).unwrap();
ValveMap::from(entries)
}
```

*Whew*, that bit of data structure manipulation really took a lot out of me.
There were a lot of moving parts there, and it took me a while to get the code
into a shape I approved of. Really, I’d prefer a version of the `ValveMap`

without nodes for the “non-productive” valves, but in the end the code is a lot
cleaner if I just leave them in and ignore them. This keeps me from needing to
re-calculate the indices or use `HashMap`

s, both of which introduce different
types of overhead.

## Part One - Elephants. Really?

The distress beacon, the one in the one space that our sensors couldn’t detect, was actually being operated by a parade of elephants. In a cave. With pipes and steam valves. There’s a lot of loose ends that are going to need to be tied up this year, that’s for sure. Ok, whatever, we’re helping elephants by spending 30 minutes opening valves in the right order. Hope it doesn’t take hours to figure out what that order should be…

```
/// Solve Day 16, Part 1
///
/// I'm not satisfied with this solution. We're essentially performing a depth-first
/// search starting from "AA" and identifying _every_ path through the notable valves
/// then returning the maximum pressure released by _any_ path. I'm quite certain
/// that there's an A* implementation that could do this much more efficiently, but
/// I'm struggling to develop an appropriate penalty function. I'll come back to this
/// one.
fn solve(input: &ValveMap) -> u32 {
// Start at valve "AA"
let state = TravelState::default();
let mut open = vec![state];
// Depth-first seach starting with "AA". Every time we hit a state where
// time has run out, we check the total pressure released and update
// the maximum pressure if necessary.
let mut max_released = 0;
// While the search stack has items remaining...
while let Some(state) = open.pop() {
// Check the state on the top of the stack. If time is up, try to update
// the max pressure released and move on to the next item in the stack.
if state.remaining == 0 {
max_released = max_released.max(state.released);
continue;
}
// If the state is an intermediate state (time still remaining), then add
// each possible next state to the stack.
for next_state in state.next_states(input) {
open.push(next_state);
}
}
// Return the maximum pressure released by any path through the valves
max_released
}
/// Represents the state of a path through the valves. Indicates current location
/// as a node index, the set of open valves, the amount of time remaining, and
/// the total pressure that will be released once time runs out given the valves
/// open.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
struct TravelState {
location: usize,
valves_open: u64,
remaining: u32,
released: u32,
}
impl Default for TravelState {
fn default() -> Self {
TravelState {
location: Default::default(),
valves_open: Default::default(),
remaining: 30, // Default to 30 minutes remaining
released: Default::default(),
}
}
}
impl TravelState {
/// Return a list of possible next states that can be reached from the
/// current state.
pub fn next_states(&self, map: &ValveMap) -> Vec<TravelState> {
let mut out = Vec::new();
// For each neighboring valve (and the distance to get to it)...
for (neighbor, distance) in map.edges[self.location].iter() {
// If the distance to the neighbor is to far to reach in the
// time remaining or that valve is already open, then skip that
// neighbor as a possible next state.
if *distance >= self.remaining {
continue;
}
let valve = map.nodes[*neighbor];
if valve.id & self.valves_open > 0 {
continue;
}
// Update the next state by subtracting the distance traveled from the
// time remaining, with one extra for opening the destination valve.
// Set the location of the next state to the neighbor index and add
// the set bit for the valve ID to the value keeping track of which
// valves are already open. When opening a new valve, add the flow rate
// of that valve times the remaining time to the total pressure released
// by this state, since we already know that valve will remain open
// for the rest of the time, releasing its flow rate each minute.
let mut new_state = *self;
new_state.location = *neighbor;
new_state.valves_open |= valve.id;
new_state.remaining -= (distance + 1);
new_state.released += (new_state.remaining * valve.flow);
out.push(new_state);
}
// If there aren't any neighbors to travel to, either because all the valves
// are already open or the travel time is more than time remaining, then we
// can just "wait it out", staying put until time runs out.
if out.is_empty() {
let mut wait_state = *self;
wait_state.remaining = 0;
out.push(wait_state)
}
out
}
}
```

Well, there you have it. I don’t *love* this approach, but it does run in ~13ms
on my computer, so it’s not *painfully* slow. I can’t shake the feeling that it
could be much better, though. Regardless, it works, and considering I’ve fallen
a bit behind my pace on solutions and blog posts due to family commitments and
the difficulty spike, I’m going to go ahead and let it go. Perhaps I’ll get a
chance to come back to it on a later day or even after Christmas. Either way,
star earned!

## Part Two - A Helping “Hand”

At least the elephants are smart! Well, one of them, anyway. Honestly, it’s a good thing that only one elephant wants to help, otherwise the computation for maximizing valve-opening efficiency could get really wild. As it it, adding in one elephant led me to relying on some…questionable… tactics.

```
/// Solve Day 16, Part 2
///
/// This part is also a bit hacky. Here we're again producing every possible end
/// state from walking through and opening valves, just in 26 minutes instead of
/// 30. With that list of possible paths through the valves, we check the paths
/// that release the most pressure for two that open different sets of valves.
/// The total pressure released from the most pressure-releasing pair of
/// non-overlapping paths is the most pressure that two actors can release working
/// together.
fn solve(input: &ValveMap) -> u32 {
// Now the initial state starts with only 26 minutes remaining.
let mut state = TravelState {
remaining: 26,
..Default::default()
};
let mut open = vec![state];
// Instead of tracking the most pressure released, we're collecting all possible
// final states.
let mut best_results = Vec::new();
while let Some(state) = open.pop() {
if state.remaining == 0 {
best_results.push(state);
continue;
}
for neighbor in state.next_states(input) {
open.push(neighbor);
}
}
// Now we sort the final states by pressure released
best_results.sort_unstable();
// How many of the most productive states should be considered? This is the
// 1337 hax. If we don't check enough of the most productive states, we'll
// get the wrong answer. So, how many should we check? Enough until we get the
// right answer! Honestly I started high and just started reducing the number
// until I got low enough that this function started producing the wrong
// answer.
let depth = 285;
// Notice that we're iterating backwards over the list of results, since
// it's sorted by ascending total pressure released. For each top result, we
// check down the list for the next result that doesn't overlap. There's a
// possibility that this wouldn't work and we'd need to find a few pairs
// this way and get their maximum, but for my input the first pair found
// through this strategy produces the correct result.
for (idx, result1) in best_results.iter().rev().enumerate().take(depth) {
for result2 in best_results.iter().rev().skip(idx + 1).take(depth) {
if result1.valves_open & result2.valves_open > 0 {
continue;
}
let max = result1.released + result2.released;
return max;
}
}
// If we get this far, we've failed.
panic!("Could not solve part two!");
}
/// Implement ordering for travel states so we can sort the list above. We'll
/// sort by total pressure released.
impl Ord for TravelState {
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.released.cmp(&other.released)
}
}
impl PartialOrd for TravelState {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
Some(self.cmp(other))
}
}
```

That’s right, I’m not going to try keeping up with the combinatoric explosion of double the possible next states for each existing state. Instead, I’m honestly using a little bit of “Christmas Faith” to assume that two of the most productive end states are mutually exclusive and represent a path for me and a path for the elephant that will, together, result in the best possible valve-turning outcome. Foolproof.

## Wrap Up

So, I was dissatisfied with my result until I started reading through other
people’s experiences today, especially considering the run times some folks
are experiencing. I *also* thought I’d find someone with a good A* implementation
for part one I could shamelessly pull from, but so far, no dice. Honestly, I
feel a bit better seeing that. I truly put a lot of effort into getting that
input into a shape I thought would work for an efficient solution, and it seems
like I succeeded there. I’m also glad I Googled for finding the shortest pairwise
distances in a graph, otherwise I was prepared to do a *bunch* of Dijkstra’s
instead of the more concise Floyd-Warshall algorithm. The Wikipedia article
was actually helpful this time, too. I usually find those algorithm articles
to be a bit dense for my taste, so that was a welcome surprise. All in all,
a tough day, but a good day.