TSP algo

Author: I761212

dynamic_programming/travelling_sales_person.py

@@ -0,0 +1,65 @@
+"""
+Dynamic Programming: Travelling Salesman Problem (TSP)
+-----------------------------------------------------
+Solves the classic TSP using the Held–Karp dynamic programming approach.
+
+Time Complexity: O(n^2 * 2^n)
+Space Complexity: O(n * 2^n)
+
+Example:
+    >>> cost = [
+    ...     [0, 10, 15, 20],
+    ...     [10, 0, 35, 25],
+    ...     [15, 35, 0, 30],
+    ...     [20, 25, 30, 0]
+    ... ]
+    >>> travelling_salesman(cost)
+    80
+"""
+
+from functools import lru_cache
+
+
+def travelling_salesman(cost_matrix: list[list[int]]) -> int:
+    """
+    Returns the minimum travel cost for visiting all cities and returning
+    to the starting city (0-indexed), using the Held–Karp DP approach.
+
+    Args:
+        cost_matrix (list[list[int]]): A square matrix where cost_matrix[i][j]
+            represents the cost of traveling from city i to city j.
+
+    Returns:
+        int: The minimum total cost of the tour.
+    """
+    n = len(cost_matrix)
+    all_visited = (1 << n) - 1  # bitmask with all cities visited
+
+    @lru_cache(maxsize=None)
+    def dp(mask: int, pos: int) -> int:
+        # Base case: all cities visited, return cost to go back to start
+        if mask == all_visited:
+            return cost_matrix[pos][0]
+
+        ans = float("inf")
+        for city in range(n):
+            # If city not yet visited
+            if not (mask & (1 << city)):
+                new_cost = cost_matrix[pos][city] + dp(mask | (1 << city), city)
+                ans = min(ans, new_cost)
+        return ans
+
+    # Start from city 0 with only it visited
+    return dp(1, 0)
+
+
+if __name__ == "__main__":
+    # Example test case
+    cost = [
+        [0, 10, 15, 20],
+        [10, 0, 35, 25],
+        [15, 35, 0, 30],
+        [20, 25, 30, 0],
+    ]
+
+    print("Minimum tour cost:", travelling_salesman(cost))