Update app.py

app.py

@@ -26,24 +26,20 @@ def held_karp_tsp(dist_matrix):
                 bits |= 1 << bit
             for next_city in subset:
                 prev_bits = bits & ~(1 << next_city)
-                min_dist = inf
-                for k in subset:
-                    if k == next_city:
-                        continue
-                    current_dist = memo[(prev_bits, k)] + dist_matrix[k][next_city]
-                    if current_dist < min_dist:
-                        min_dist = current_dist
-                memo[(bits, next_city)] = min_dist
+                if (prev_bits, next_city) in memo:
+                    min_dist = memo[(prev_bits, next_city)] + dist_matrix[next_city][0]
+                    memo[(bits, next_city)] = min_dist
 
     # Find optimal tour
     bits = (2 ** n - 1) - 1
     min_tour_cost = inf
     last_city = None
     for k in range(1, n):
-        tour_cost = memo[(bits, k)] + dist_matrix[k][0]
-        if tour_cost < min_tour_cost:
-            min_tour_cost = tour_cost
-            last_city = k
+        if (bits, k) in memo:
+            tour_cost = memo[(bits, k)] + dist_matrix[k][0]
+            if tour_cost < min_tour_cost:
+                min_tour_cost = tour_cost
+                last_city = k
 
     # Backtrack to find the full tour
     tour = [0]
@@ -51,6 +47,8 @@ def held_karp_tsp(dist_matrix):
     for _ in range(n - 1):
         tour.append(last_city)
         bits &= ~(1 << last_city)
+        if bits == 0:
+            break
         next_city = min(
             [(memo[(bits, k)] + dist_matrix[k][last_city], k) for k in range(n) if (bits, k) in memo],
             key=lambda x: x[0],