|
| 1 | +import numpy as np |
| 2 | +from scipy.optimize import linprog |
| 3 | +from collections import deque |
| 4 | + |
| 5 | + |
| 6 | +with open("input") as f: |
| 7 | + ls = f.read().strip().split("\n") |
| 8 | + |
| 9 | +tasks = [] |
| 10 | +for l in ls: |
| 11 | + toggles, *buttons, counters = l.split() |
| 12 | + toggles = set(i for i, x in enumerate(toggles[1:-1]) if x == "#") |
| 13 | + moves = [{int(x) for x in b[1:-1].split(",")} for b in buttons] |
| 14 | + counters = tuple(map(int, counters[1:-1].split(","))) |
| 15 | + tasks.append((toggles, moves, counters)) |
| 16 | + |
| 17 | + |
| 18 | +def solve1(goal, moves): |
| 19 | + q = deque() |
| 20 | + q.append((set(), 0)) |
| 21 | + seen = set() |
| 22 | + while q: |
| 23 | + curr, steps = q.popleft() |
| 24 | + if curr == goal: |
| 25 | + return steps |
| 26 | + for m in moves: |
| 27 | + newset = curr ^ m |
| 28 | + if newset in seen: |
| 29 | + continue |
| 30 | + seen.add(frozenset(newset)) |
| 31 | + q.append((newset, steps + 1)) |
| 32 | + |
| 33 | + |
| 34 | +print(sum(solve1(goal, moves) for goal, moves, _ in tasks)) |
| 35 | + |
| 36 | + |
| 37 | +# Part 2 |
| 38 | +def solve2(goal, moves): |
| 39 | + c = np.ones(len(moves)) |
| 40 | + A_eq = [] |
| 41 | + b_eq = [] |
| 42 | + for i in range(len(goal)): |
| 43 | + A_eq.append([1 if i in move else 0 for move in moves]) |
| 44 | + b_eq.append(goal[i]) |
| 45 | + A_eq = np.array(A_eq) |
| 46 | + b_eq = np.array(b_eq) |
| 47 | + res = linprog(c, A_eq=A_eq, b_eq=b_eq, method="highs", integrality=True) |
| 48 | + return res.fun |
| 49 | + |
| 50 | + |
| 51 | +print(sum(solve2(counters, moves) for _, moves, counters in tasks)) |
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