Create travel_salesman.py

dynamic_programming/travel_salesman.py

@@ -0,0 +1,58 @@
+"""
+Solves the Travelling Salesman Problem (TSP) using dynamic programming and bitmasking.
+
+Example:
+distances = [
+    [0, 10, 15, 20],
+    [10, 0, 35, 25],
+    [15, 35, 0, 30],
+    [20, 25, 30, 0]
+]
+Output: 80
+"""
+
+from functools import lru_cache
+
+
+def tsp(distances: list[list[int]]) -> int:
+    """
+    Solves TSP using dynamic programming.
+
+    >>> tsp([[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
+    80
+    >>> tsp([[0, 29, 20, 21], [29, 0, 15, 17], [20, 15, 0, 28], [21, 17, 28, 0]])
+    69
+    >>> tsp([[0, 10, -15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
+    Traceback (most recent call last):
+        ...
+    ValueError: Distance cannot be negative
+    """
+    n = len(distances)
+    if any(distances[i][j] < 0 for i in range(n) for j in range(n)):
+        raise ValueError("Distance cannot be negative")
+
+    visited_all = (1 << n) - 1
+
+    @lru_cache(None)
+    def visit(city: int, mask: int) -> int:
+        """Recursively calculates the minimum cost of visiting all cities."""
+        if mask == visited_all:
+            return distances[city][0]  # Return to start
+
+        min_cost = float('inf')
+        for next_city in range(n):
+            if not mask & (1 << next_city):  # If next_city is unvisited
+                new_cost = distances[city][next_city] + visit(
+                    next_city, mask | (1 << next_city)
+                )
+                min_cost = min(min_cost, new_cost)
+        return min_cost
+
+    return visit(0, 1)  # Start from city 0 with only city 0 visited
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
+    print(f"{tsp([[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]]) = }")
+    print(f"{tsp([[0, 29, 20, 21], [29, 0, 15, 17], [20, 15, 0, 28], [21, 17, 28, 0]]) = }")