Day 10

Author: Vic-Nas

aoc/2025/10/normal/10_normal.vn.py

@@ -1,4 +1,96 @@
 # Advent of code Day 10
 # https://adventofcode.com/2025/day/10
 # 10/12/2025
-    
+    
+from functools import cache    
+import heapq
+from collections import defaultdict
+from tqdm.auto import tqdm
+from collections import deque
+
+def parse(buttons: str):
+    buttons = buttons.strip().split()
+    goal = tuple(map(lambda x: int(x == "#"),
+        buttons[0][1:-1]))
+    size = len(goal)
+    req = tuple(map(int, buttons[-1][1:-1].split(",")))
+    buttons = list(map(
+        lambda s: tuple(map(int, s[1:-1].split(","))),
+        buttons[1:-1]))
+    for i in range(len(buttons)):
+        r = [0 for _ in range(size)]
+        for j in buttons[i]:
+            r[j] = 1
+        buttons[i] = tuple(r)
+    return goal, buttons, req
+
+
+with open("input.txt") as file:
+    inp = list(
+        map(
+            parse,
+            file.readlines()
+        )
+    )
+   
+@cache
+def apply(button, state, mod=2):
+    res = list(state)
+    for i in range(len(res)):
+        res[i] += button[i]
+        if mod:
+            res[i] %= mod
+    return tuple(res)
+    
+res1, res2 = 0, 0
+for goal, buttons, req in tqdm(inp):
+    n = len(goal)
+    start = tuple(0 for _ in range(n))
+    visited = defaultdict(bool)
+    heap = [(0, start)] 
+    
+    while heap:
+        presses, state = heapq.heappop(heap)
+        
+        if visited[state]: continue
+        visited[state] = True
+        
+        if state == goal:
+            res1 += presses
+            break
+        
+        for button in buttons:
+            newState = apply(button, state)
+            if visited[newState]: continue
+            
+            heapq.heappush(
+                heap,
+                (presses+1, newState)
+            )
+
+from pulp import *
+
+res2 = 0
+for goal, buttons, req in tqdm(inp):
+    # Create integer variables (x[i] = times button i is pressed)
+    x = [LpVariable(f"x{i}", 0, cat='Integer') for i in range(len(buttons))]
+    
+    # Create optimization problem (minimization by default)
+    prob = LpProblem()
+    
+    # Objective: minimize total button presses
+    prob += lpSum(x)
+    
+    # Constraints: for each dimension, button presses must sum to goal
+    for i in range(len(goal)):
+        prob += lpSum(x[j] * buttons[j][i] for j in range(len(buttons))) == req[i]
+    
+    # Solve with no output
+    prob.solve(PULP_CBC_CMD(msg=0))
+    
+    # If optimal solution found, add to result
+    if prob.status == 1:
+        res2 += int(value(prob.objective))
+        
+                
+print(res1, res2)