Day 7: Bridge Repair

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FAQ

1 point

I’m way behind, but I’m trying to learn F#.

I’m using the library Combinatorics in dotnet, which I’ve used in the past, generate in this case every duplicating possibility of the operations. I the only optimization that I did was to use a function to concatenate numbers without converting to strings, but that didn’t actually help much.

I have parser helpers that use ReadOnlySpans over strings to prevent unnecessary allocations. However, here I’m adding to a C# mutable list and then converting to an FSharp (linked) list, which this language is more familiar with. Not optimal, but runtime was pretty good.

I’m not terribly good with F#, but I think I did ok for this challenge.

F#

// in another file:
let concatenateLong (a:Int64) (b:Int64) : Int64 =
    let rec countDigits (n:int64) =
        if n = 0 then 0
        else 1 + countDigits (n / (int64 10))   

    let bDigits = if b = 0 then 1 else countDigits b
    let multiplier = pown 10 bDigits |> int64
    a * multiplier + b

// challenge file
type Operation = {Total:Int64; Inputs:Int64 list }

let parse (s:ReadOnlySpan<char>) : Operation =
    let sep = s.IndexOf(':')
    let total = Int64.Parse(s.Slice(0, sep))
    let inputs = System.Collections.Generic.List<Int64>()
    let right:ReadOnlySpan<char> = s.Slice(sep + 1).Trim()

   // because the Split function on a span returns a SpanSplitEnumerator, which is a ref-struct and can only live on the stack, 
   // I can't use the F# list syntax here
    for range in right.Split(" ") do
        inputs.Add(Int64.Parse(sliceRange right range))
        
    {Total = total; Inputs = List.ofSeq(inputs) }

let part1Ops = [(+); (*)]

let execute ops input =
    input
    |> PSeq.choose (fun op ->
        let total = op.Total
        let inputs = op.Inputs
        let variations = Variations(ops, inputs.Length - 1, GenerateOption.WithRepetition)
        variations
        |> Seq.tryFind (fun v ->
            let calcTotal = (inputs[0], inputs[1..], List.ofSeq(v)) |||> List.fold2 (fun acc n f -> f acc n) 
            calcTotal = total
            )
        |> Option.map(fun _ -> total)
        )
    |> PSeq.fold (fun acc n -> acc + n) 0L

let part1 input =
    (read input parse)
    |> execute part1Ops

let part2Ops = [(+); (*); concatenateLong]

let part2 input = (read input parse) |> execute part2Ops

The Gen0 garbage collection looks absurd, but Gen0 is generally considered β€œfree”.

Method Mean Error StdDev Gen0 Gen1 Allocated
Part1 19.20 ms 0.372 ms 0.545 ms 17843.7500 156.2500 106.55 MB
Part2 17.94 ms 0.355 ms 0.878 ms 17843.7500 156.2500 106.55 MB

V2 - concatenate numbers did little for the runtime, but did help with Gen1 garbage, but not the overall allocation.

Method Mean Error StdDev Gen0 Gen1 Allocated
Part1 17.34 ms 0.342 ms 0.336 ms 17843.7500 125.0000 106.55 MB
Part2 17.24 ms 0.323 ms 0.270 ms 17843.7500 93.7500 106.55 MB
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7 points
*

Uiua

This turned out to be reasonably easy in Uiua, though this solution relies on macros which maybe slow it down.

(edit: removing one macro sped it up quite a bit)

(edit2: Letting Uiua build up an n-dimensional array turned out to be the solution, though sadly my mind only works in 3 dimensions. Now runs against the live data in around 0.3 seconds.)

Try it here

Data   ← ⊜(β–‘βŠœβ‹•βŠΈ(¬∈": "))βŠΈβ‰ @\n "190: 10 19\n3267: 81 40 27\n83: 17 5\n156: 15 6\n7290: 6 8 6 15\n161011: 16 10 13\n192: 17 8 14\n21037: 9 7 18 13\n292: 11 6 16 20"
Calib! ← β‰‘β—‡βŠ’β–½βŠΈβ‰‘β—‡(βˆˆβ™­/[^0]:Β°βŠ‚) # Calibration targets which can be constructed from their values.
&p/+Calib!βŠƒ(+|Γ—)Data
&p/+Calib!βŠƒ(+|Γ—|+×ⁿ:10+1βŒŠβ‚™β‚β‚€,)Data
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3 points

Thanks to your solution I learned more about how to use reduce :D

My solution did work for the example input but not for the actual one. When I went here and saw this tiny code block and you saying

This turned out to be reasonably easy

I was quite taken aback. And it’s so much better performance-wise too :D (well, until part 2 comes along in my case. Whatever this black magic is you used there is too high for my fried brain atm)

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2 points

Haha, sorry about that, it does seem quite smug :-) I went into it expecting it to be a nightmare of boxes and dimensions, but finding it something I could deal with was a massive relief. Of course once I had a working solution I reversed it back into a multi-dimensional nightmare. That’s where the performance gains came from: about 10x speedup from letting Uiua build up as many dimensions as it needed before doing a final deshaping.

I enjoyed reading a different approach to this, and thanks for reminding me that β‹•$"__" exists, that’s a great idiom to have up your sleeve.

Let me know if there’s any bits of my solution that you’d like me to talk you through.

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2 points

No worries, it does seem a lot less difficult in hindsight now, my mind just blanked at what I expected to be a lot more code :))

That performance improvement is amazing, I’ll definitely take a look at how that works in detail later. Just gotta recover from the mental stretch gymnastics trying to remember the state of the stack at different code positions

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3 points

I feel like I need a Rosetta stone to read this code

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1 point
2 points

thanks that indeed is a Rosetta stone

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4 points
*

Haskell

A surprisingly gentle one for the weekend! Avoiding string operations for concatenate got the runtime down below one second on my machine.

import Control.Arrow
import Control.Monad
import Data.List
import Data.Maybe

readInput :: String -> [(Int, [Int])]
readInput = lines >>> map (break (== ':') >>> (read *** map read . words . tail))

equatable :: [Int -> Int -> Int] -> (Int, [Int]) -> Bool
equatable ops (x, y : ys) = elem x $ foldM apply y ys
  where
    apply a y = (\op -> a `op` y) <$> ops

concatenate :: Int -> Int -> Int
concatenate x y = x * mag y + y
  where
    mag z = fromJust $ find (> z) $ iterate (* 10) 10

main = do
  input <- readInput <$> readFile "input07"
  mapM_
    (print . sum . map fst . (`filter` input) . equatable)
    [ [(+), (*)],
      [(+), (*), concatenate]
    ]
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3 points

Love the fold on the list monad to apply the operations.

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2 points
*

Since all operations increase the accumulator, I tried putting a guard (a <= x) in apply, but it doesn’t actually help all that much (0.65s -> 0.43s).

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2 points
*

0.65 -> 0.43 sounds pretty strong, isn’t that a one-fourth speedup?

Edit: I was able to achieve a 30% speed improvement using this on my solution

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2 points

It’s not insignificant, sure, but I’d prefer 10x faster :D

Plus I’m not sure it’s worth the loss of generality and readability. It is tempting to spend hours chasing this kind of optimization though!

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2 points

I wanted to this the way yo did, by repeatedly applying functions, but I didn’t dare to because I like to mess up and spend some minutes debugging signatures, may I ask what your IDE setup is for the LSP-Hints with Haskell?
Setting up on my PC was a little bit of a pain because it needed matching ghc and ghcide versions, so I hadn’t bothered doing it on my Laptop yet.

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3 points

I use neovim with haskell-tools.nvim plugin. For ghc, haskell-language-server and others I use nix which, among other benefits makes my development environment reproducible and all haskellPackages are built on the same version so there are no missmatches.

But, as much as I love nix, there are probably easier ways to setup your environment.

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2 points

I just checked and I have haskell-tools.nvim on my PC but it somehow crashes the default config of the autocompletion for me, which I am too inexperienced to debug. I’ll try it nonetheless, since I don’t have autocompletion on the laptop anyways, thank you for the suggestion!

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2 points

Ah, well, I have a bit of a weird setup. GHC is 9.8.4, built from git. I’m using HLS version 2.9.0.1 (again built from git) under Emacs with the LSP and flycheck packages. There are probably much easier ways of getting it to work :)

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2 points

I envy emacs for all of its modes, but I don’t think I’m relearning the little I know about vi. Thank you for the answer on the versions and building!

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4 points

Haskell

import Control.Arrow
import Data.Char
import Text.ParserCombinators.ReadP

numP = read <$> munch1 isDigit
parse = endBy ((,) <$> (numP <* string ": ") <*> sepBy numP (char ' ')) (char '\n')

valid n [m] = m == n
valid n (x : xs) = n > 0 && valid (n - x) xs || (n `mod` x) == 0 && valid (n `div` x) xs

part1 = sum . fmap fst . filter (uncurry valid . second reverse)

concatNum r = (+r) . (* 10 ^ digits r)
    where
        digits = succ . floor . logBase 10 . fromIntegral

allPossible [n] = [n]
allPossible (x:xs) = ((x+) <$> rest) ++ ((x*) <$> rest) ++ (concatNum x <$> rest)
    where
        rest = allPossible xs

part2 = sum . fmap fst . filter (uncurry elem . second (allPossible . reverse))

main = getContents >>= print . (part1 &&& part2) . fst . last . readP_to_S parse
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2 points

Oooh, some nice number theory going on there!

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4 points
*

Python

It is a tree search

def parse_input(path):

  with path.open("r") as fp:
    lines = fp.read().splitlines()

  roots = [int(line.split(':')[0]) for line in lines]
  node_lists = [[int(x)  for x in line.split(':')[1][1:].split(' ')] for line in lines]

  return roots, node_lists

def construct_tree(root, nodes, include_concat):

  levels = [[] for _ in range(len(nodes)+1)]
  levels[0] = [(str(root), "")]
  # level nodes are tuples of the form (val, operation) where both are str
  # val can be numerical or empty string
  # operation can be *, +, || or empty string

  for indl, level in enumerate(levels[1:], start=1):

    node = nodes[indl-1]

    for elem in levels[indl-1]:

      if elem[0]=='':
        continue

      if elem[0][-len(str(node)):] == str(node) and include_concat:
        levels[indl].append((elem[0][:-len(str(node))], "||"))
      if (a:=int(elem[0]))%(b:=int(node))==0:
        levels[indl].append((str(int(a/b)), '*'))
      if (a:=int(elem[0])) - (b:=int(node))>0:
        levels[indl].append((str(a - b), "+"))

  return levels[-1]

def solve_problem(file_name, include_concat):

  roots, node_lists = parse_input(Path(cwd, file_name))
  valid_roots = []

  for root, nodes in zip(roots, node_lists):

    top = construct_tree(root, nodes[::-1], include_concat)

    if any((x[0]=='1' and x[1]=='*') or (x[0]=='0' and x[1]=='+') or
           (x[0]=='' and x[1]=='||') for x in top):

      valid_roots.append(root)

  return sum(valid_roots)
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1 point
*

I asked ChatGPT to explain your code and mentioned you said it was a binary search. idk why, but it output a matter of fact response that claims you were wrong. lmao, I still don’t understand how your code works

This code doesn’t perform a classic binary search. Instead, it uses each input node to generate new possible states or β€œbranches,” forming a tree of transformations. At each level, it tries up to three operations on the current value (remove digits, divide, subtract). These expansions create multiple paths, and the code checks which paths end in a successful condition. While the author may have described it as a β€œbinary search,” it’s more accurately a state-space search over a tree of possible outcomes, not a binary search over a sorted data structure.

I understand it now! I took your solution and made it faster. it is now like 36 milliseconds faster for me. which is interesting because if you look at the code. I dont process the entire list of integers. I sometimes stop prematurely before the next level, clear it, and add the root. I don’t know why but it just works for my input and the test input.

code
def main(input_data):
    input_data = input_data.replace('\r', '')
    parsed_data = {int(line[0]): [int(i) for i in line[1].split()[::-1]] for line in [l.split(': ') for l in input_data.splitlines()]}
    part1 = 0
    part2 = 0
    for item in parsed_data.items():
        root, num_array = item
        part_1_branches = [set() for _ in range(len(num_array)+1)]
        part_2_branches = [set() for _ in range(len(num_array)+1)]
        part_1_branches[0].add(root)
        part_2_branches[0].add(root)
        for level,i in enumerate(num_array):
            if len(part_1_branches[level]) == 0 and len(part_2_branches[level]) == 0:
                break

            for branch in part_1_branches[level]:
                if branch == i:
                    part_1_branches[level+1] = set() # clear next level to prevent adding root again
                    part1 += root
                    break
                if branch % i == 0:
                    part_1_branches[level+1].add(branch//i)
                if branch - i > 0:
                    part_1_branches[level+1].add(branch-i)

            for branch in part_2_branches[level]:
                if branch == i or str(branch) == str(i):
                    part_2_branches[level+1] = set() # clear next level to prevent adding root again
                    part2 += root
                    break
                if branch % i == 0:
                    part_2_branches[level+1].add(branch//i)
                if branch - i > 0:
                    part_2_branches[level+1].add(branch-i)
                if str(i) == str(branch)[-len(str(i)):]:
                    part_2_branches[level+1].add(int(str(branch)[:-len(str(i))]))
    print("Part 1:", part1, "\nPart 2:", part2)
    return [part1, part2]

if __name__ == "__main__":
    with open('input', 'r') as f:
        main(f.read())

however what I notice is that the parse_input causes it to be the reason why it is slower by 20+ milliseconds. I find that even if I edited your solution like so to be slightly faster, it is still 10 milliseconds slower than mine:

code
def parse_input():

  with open('input',"r") as fp:
    lines = fp.read().splitlines()

  roots = [int(line.split(':')[0]) for line in lines]
  node_lists = [[int(x) for x in line.split(': ')[1].split(' ')] for line in lines]

  return roots, node_lists

def construct_tree(root, nodes):
    levels = [[] for _ in range(len(nodes)+1)]
    levels[0] = [(root, "")]
    # level nodes are tuples of the form (val, operation) where both are str
    # val can be numerical or empty string
    # operation can be *, +, || or empty string

    for indl, level in enumerate(levels[1:], start=1):

        node = nodes[indl-1]

        for elem in levels[indl-1]:
            if elem[0]=='':
                continue

            if (a:=elem[0])%(b:=node)==0:
                levels[indl].append((a/b, '*'))
            if (a:=elem[0]) - (b:=node)>0:
                levels[indl].append((a - b, "+"))

    return levels[-1]


def construct_tree_concat(root, nodes):
    levels = [[] for _ in range(len(nodes)+1)]
    levels[0] = [(str(root), "")]
    # level nodes are tuples of the form (val, operation) where both are str
    # val can be numerical or empty string
    # operation can be *, +, || or empty string

    for indl, level in enumerate(levels[1:], start=1):

        node = nodes[indl-1]

        for elem in levels[indl-1]:
            if elem[0]=='':
                continue

            if elem[0][-len(str(node)):] == str(node):
                levels[indl].append((elem[0][:-len(str(node))], "||"))
            if (a:=int(elem[0]))%(b:=int(node))==0:
                levels[indl].append((str(int(a/b)), '*'))
            if (a:=int(elem[0])) - (b:=int(node))>0:
                levels[indl].append((str(a - b), "+"))

    return levels[-1]

def solve_problem():

  roots, node_lists = parse_input()
  valid_roots_part1 = []
  valid_roots_part2 = []

  for root, nodes in zip(roots, node_lists):
    
    top = construct_tree(root, nodes[::-1])

    if any((x[0]==1 and x[1]=='*') or (x[0]==0 and x[1]=='+') for x in top):
      valid_roots_part1.append(root)
      
    top = construct_tree_concat(root, nodes[::-1])

    if any((x[0]=='1' and x[1]=='*') or (x[0]=='0' and x[1]=='+') or (x[0]=='' and x[1]=='||') for x in top):

      valid_roots_part2.append(root)

  return sum(valid_roots_part1),sum(valid_roots_part2)
  
if __name__ == "__main__":
    print(solve_problem())
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2 points
*

Wow I got thrashed by chatgpt. Strictly speaking that is correct, it is more akin to Tree Search. But even then not strictly because in tree search you are searching through a list of objects that is known, you build a tree out of it and based on some conditions eliminate half of the remaining tree each time. Here I have some state space (as chatgpt claims!) and again based on applying certain conditions, I eliminate some portion of the search space successively (so I dont have to evaluate that part of the tree anymore). To me both are very similar in spirit as both methods avoid evaluating some function on all the possible inputs and successively chops off a fraction of the search space. To be more correct I will atleast replace it with tree search though, thanks. And thanks for taking a close look at my solution and improving it.

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1 point

idk why my gpt decided to be like that. I expected a long winded response with a little bit of ai hallucinations. I was flabbergasted, and just had to post it.

I seemingly realized that working forward through the list of integers was inefficient for me to do, and I was using multiprocessing to do it too! which my old solution took less than 15 seconds for my input. your solution to reverse the operations and eliminate paths was brilliant and made it solve it in milliseconds without spinning up my fans, lol

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