Python贪婪算法

贪婪算法

• 每步均选择局部的最优解，重复此过程，最终即得到全局的最优解
• 简而言之就是每步都采用最优解

优点：

• 简单易行

缺点：

• 并非在所有情况下都奏效

经典的问题：

1. 背包问题
2. 集合覆盖问题

贪婪算法下的近似算法解决集合覆盖问题

states_needed = set(["mt", "wa", "or", "id", "nv", "ut", "ca", "az"])stations = {}
stations["kone"] = set(["id", "nv", "ut"])
stations["ktwo"] = set(["wa", "id", "mt"])
stations["kthree"] = set(["or", "nv", "ca"])
stations["kfour"] = set(["nv", "ut"])
stations["kfive"] = set(["ca", "az"])
final_stations = set()while states_needed:best_station = Nonestates_covered = set()for station, states in stations.items():covered = states_needed & statesif len(covered) > len(states_covered):best_station = stationstates_covered = coveredstates_needed -= states_coveredfinal_stations.add(best_station)print(final_stations)