【LeetCode】1029. Two City Scheduling 解题报告(Python)
作者: 负雪明烛
id: fuxuemingzhu
个人博客: http://fuxuemingzhu.cn/
题目地址:https://leetcode.com/problems/two-city-scheduling/
题目描述
There are 2N
people a company is planning to interview. The cost of flying the i
-th person to city A
is costs[i][0]
, and the cost of flying the i
-th person to city B
is costs[i][1]
.
Return the minimum cost to fly every person to a city such that exactly N
people arrive in each city.
Example 1:
Input: [[10,20],[30,200],[400,50],[30,20]]
Output: 110
Explanation:
The first person goes to city A for a cost of 10.
The second person goes to city A for a cost of 30.
The third person goes to city B for a cost of 50.
The fourth person goes to city B for a cost of 20.
The total minimum cost is 10 + 30 + 50 + 20 = 110 to have half the people interviewing in each city.
Note:
1 <= costs.length <= 100
It is guaranteed that costs.length is even.
1 <= costs[i][0], costs[i][1] <= 1000
题目大意
给出了偶数个候选人去A和B两个城市的花费,现在要合理分配,让两个城市的人一样多,并且总花费最少。求最少花费。
解题方法
小根堆
思路怎么来的,是我划了一个表格:
编号 | 甲 | 乙 | 丙 | 丁 |
---|---|---|---|---|
去A的花费 | 10 | 30 | 400 | 30 |
去B的花费 | 20 | 200 | 50 | 40 |
B-A | +20 | +170 | -350 | -10 |
根据表格我们可以想到,如果让丙去A,那么会比让丙去B多花350,这样多花费的钱划不来。所以,我们一定让去B比去A花费节省最多的人去B,反之,去A比去B花费节省最多的人去A。故这是一个贪心算法。
具体做法是我们求出每个人B-A的值,让去B能省下最省钱的一半人先去B,剩下的一半人去A.我们可以使用堆或者排序去完成这个事情。
class Solution(object):
def twoCitySchedCost(self, costs):
"""
:type costs: List[List[int]]
:rtype: int
"""
heap = []
for i, cost in enumerate(costs):
heapq.heappush(heap, (cost[1] - cost[0], i))
res = 0
count = 0
while heap:
cost, pos = heapq.heappop(heap)
if count < len(costs) / 2:
res += costs[pos][1]
else:
res += costs[pos][0]
count += 1
return res
排序
道理和上面类似。
class Solution(object):
def twoCitySchedCost(self, costs):
"""
:type costs: List[List[int]]
:rtype: int
"""
_len = len(costs)
cost_diff = []
for i, cost in enumerate(costs):
cost_diff.append((cost[1] - cost[0], i))
cost_diff.sort()
res = 0
count = 0
for i, (diff, pos) in enumerate(cost_diff):
if i < _len / 2:
res += costs[pos][1]
else:
res += costs[pos][0]
return res
日期
2019 年 8 月 31 日 —— 赶在月底做个题