Day 6 and 7 are fantastic for that. It's a learning moment, where you can get the benefit of thinking about the problem for a few minutes instead of bruteforcing gives you a dramatic speed up.

When I read the Day 7, I saw bruteforcing would lead to bad perfs. Then I remembered that in high school, I learned a formula to calculate the nth term of a sequence without having to process the entire sequence.

I couldn't remember the formula, nor the name of the concept, so I google around until I found some tutorials, and relearned what I was taught as a child: arithmetic sums.

The consumption for a crab can then be calculated in constant time:

    def crab_consumption(crab, target):
        n = abs(target - crab) - 1 
        return (n**2 + 3*n + 2) / 2

And the Python solution finishes instantly.

Bottom line, I could keep using Python for all problems and benefit from the amazing productivity of it.

I've been using python since 2.4 now, and it's not always fast enough. But it very, very often, is.

Interesting, I'll have to look at that. Actual algorithms are a big weak point for me, I'm not a developer by day, so I don't spend a lot of time learning code practices or computer science/math topics. This was my solution to Day 7 Part 2 (positions = sorted int list of input data):

    def calculate_fuel(positions):
        fuel = None
        low_fuel_value = None

        calculated = dict()
        for value in range(positions[0], positions[-1]+1):
            consumed = 0
            for position in positions:
                diff = abs(value - position)
                try:
                    consumed += calculated[diff]
                except KeyError:
                    if position != value:
                        consumption = sum([x for x in range(1,diff+1)])
                        calculated[diff] = consumption
                        consumed += consumption
            if fuel:
                if consumed < fuel:
                    fuel = consumed
                    low_fuel_value = value
            else:
                fuel = consumed
                low_fuel_value = value

        print(f"Aligning to: {low_fuel_value}")
        return fuel

And Day 6 I fell for the bruteforce bait and had the thought "It can't be as easy as changing 80 to 256, right?". Then I realized the pain I had created for myself. BUT! My 6p2 code ran faster than my 6p1 by a good margin, which I was happy about.

Can even calculate the target without bruteforcing. . n^2+n is approximated by n^2 when n is large. Can take the mean of all the distances and use that (or perhaps +/- 1).

    val crabs = lines.first().split(",").map { it.toInt() }
    val avg = crabs.sum() / crabs.size
    return crabs.sumOf { abs(it - avg).let {dst -> (dst * (dst + 1)) / 2} } 

And similarly for part1 take the median. Why it kinda works:

Part1: The median I felt made sense intuitively, as in my head I thought about an example ala 1,1,3,100. Never makes sense to use x>3, because even though the crab at x=100 then can walk shorter, there are 3 others then having to walk longer. And x=1,2or3 doesn't matter, just symmetrically changes which side has to walk one step less or one step more.

And for part2 I thought similar, except the cost is exponential and therefore I want to minimize the avg move and not the total moves, thus taking the average.

The better optimization with Python is to not use python. Half joking. If you want awesome performances, transform your problem into a numerical one, and use Numpy. Numpy is awesome. I always miss it when using other languages than Python.

I don't understand your solution to Day7. What kind of dictionary, and how come even a naive solution would be so slow?

My Kotlin solution runs in about a second. And it was even so stupid that I didn't calculate the sum of the arithmetic series directly, but through a loop. Can't fathom something being slower.

    line.split(",").map { it.toInt() }.let { crabs ->
        (0..crabs.maxOf { it }).minOf { pos ->
            crabs.sumOf {
                (0..abs(it - pos)).sum() // slower than calculating arithmetic sum, but quicker to write 
            }
        }
    }

My proper Kotlin solution runs in less than a ms, though.

I do think that "it's good that python is slow because it forces you to optimize" is a weird take, though.

> I do think that "it's good that python is slow because it forces you to optimize" is a weird take, though.

My take wasn't that it forces you to, but that non-optimal code paths can be greatly exaggerated in comparison to other languages, particularly compiled ones. You can still ignore it (I mean, within reason), but it can give you that extra push to really look a bit deeper to understand what's going on. And of course, there's optimized libraries written in C/C++ that you can take advantage of for even better number crunching than standard CPython.

> What kind of dictionary, and how come even a naive solution would be so slow?

My naive solution was literally going through every single element for every loop and not storing any data besides the fuel buildup and the alignment number that generated it. The dictionary was added to act as a cache to store already computed fuel consumption values, initially per-loop then moved one level up to be global (because the summations wouldn't be different).

I'm not saying my method (posted in a sibling comment) is the best solution, but it's the way my brain walked through the problem.

Posted my optimized Kotlin solution in that sibling thread :)

Cool of you to participate without being a developer! Lots of computer science topics makes it easier, so hard without knowing of them. For instance graph searching / Dijkstra has beem relevant this week.

Yeah, I was doing a CS minor in college but had to drop as it was consuming too much of my time from my other discipline in the non-intro courses. Big(O)/time complexity were my usual failings in the intro algorithms course I took.

I'm not unfamiliar with programming, but I come from the sysadmin side of things. "Glue" work is usually where things are focused and the 'fun' nitty-gritty of algorithms can be a bit out-of-scope, though I'm not a sysadmin in my current role anymore so any dev-related work I do is purely personal now.

I've had to take a break from AoC, only got up through Day 10, but didn't get P2 for 8 and 9. It's a fun way to keep the mind going and to slip back into the coding space to at least not lose skills, even if the solutions are simple/non-optimal.