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- Type de membre: Membre régulier
- Vues du profil: 226 vues
- Copains: 0 ami
- Dernière mise à jour: 8 février 2024
- Rejoint: 31 janvier 2024
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Leo Smith a commenté 's .8 février 2024
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- 8 février 2024
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- 3 février 2024 ·
Hey fellow coders! Can someone help me on this?
Write a function using Dynamic Programming to solve the Traveling Salesman Problem. Input a list of cities with coordinates and get the optimal route plus the minimum distance.-
Leo Smith a réagi à cela.
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thomas brown Hey fellow coders! Can someone help me on this?
Write a function using Dynamic Programming to solve the Traveling Salesman Problem. Input a list of cities with coordinates and get the optimal route plus the minimum distance.
import itertools
import sys
Write a function using Dynamic Programming to solve the Traveling Salesman Problem. Input a list of cities with coordinates and get the optimal route plus the minimum distance.
import itertools
import sys
def distance(city1, city2):
"""
Calculate the Euclidean distance between two cities.
"""
x1, y1 = city1
x2, y2 = city2
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
def tsp_dynamic_programming(cities):
"""
Solve the Traveling Salesman Problem using Dynamic Programming.
"""
n = len(cities)
all_cities = set(range
)
# Create a memoization table to store optimal distances
memo = {}
# Base case: starting from city 0, with no other cities visited
memo[(frozenset([0]), 0)] = 0
# Iterate over all subsets of cities and compute the optimal route
for r in range(1, n):
for subset in itertools.combinations(all_cities - {0}, r):
subset = frozenset(subset)
for k in... Moins- 4 février 2024
- · 1 aime ça
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thomas brown Hello fellow coding enthusiasts!
Lets solve this classic problem involves finding the most efficient route that visits each city exactly once and returns to the original city while minimizing the total distance traveled.
Here's a Python function to...plusHello fellow coding enthusiasts!
Lets solve this classic problem involves finding the most efficient route that visits each city exactly once and returns to the original city while minimizing the total distance traveled.
Here's a Python function to tackle the Traveling Salesman Problem:
import itertools
def calculate_distance(city1, city2):
# Assuming city1 and city2 are tuples containing (x, y) coordinates
return ((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) ** 0.5
def tsp_dynamic_programming(cities):
n = len(cities)
all_cities = set(range)
# Create a memoization table to store optimal distances
memo = {}
def tsp_helper(curr_city, remaining_cities):
if not remaining_cities:
return 0, [curr_city]
if (curr_city, remaining_cities) in memo:
return memo[(curr_city, remaining_cities)]
min_distance = float('inf')
min_path = None
for next_city in remaining_cities:
distance_to_next =... Moins- 5 février 2024
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What truly sets ProgrammingHomeworkHelp.com apart is the caliber of their experts. I was paired with a highly knowledgeable professional who not only demonstrated expertise in cryptography but also a keen understanding of the specific requirements of my assignment. Their commitment to delivering top-notch solutions was evident in the detailed and... Moins - Leo Smith vient de s'inscrire. Dis bonjour!31 janvier 2024
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- Nom de famille Smith
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- Anniversaire 13 juin 2000
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