18. 4Sum; combination sum

本文详细介绍了4Sum算法的实现,通过递归解决N-Sum问题,并探讨了组合求和的解决方案。代码示例清晰展示了如何寻找目标和的四数之和,以及如何从候选数中找到所有可能的组合,使它们的和等于目标值。
18. 4Sum
class Solution(object):
def fourSum(self, nums, target):
nums.sort()
results = []
self.findNsum(nums, target, 4, [], results)
return results

def findNsum(self, nums, target, N, result, results):
if len(nums) < N or N < 2: return

# solve 2-sum
if N == 2:
l,r = 0,len(nums)-1
while l < r:
if nums[l] + nums[r] == target:
results.append(result + [nums[l], nums[r]])
l += 1
r -= 1
while l < r and nums[l] == nums[l - 1]:
l += 1
while r > l and nums[r] == nums[r + 1]:
r -= 1
elif nums[l] + nums[r] < target:
l += 1
else:
r -= 1
else:
for i in range(0, len(nums)-N+1): # careful about range
if target < nums[i]*N or target > nums[-1]*N: # take advantages of sorted list
break
if i == 0 or i > 0 and nums[i-1] != nums[i]: # recursively reduce N
self.findNsum(nums[i+1:], target-nums[i], N-1, result+[nums[i]], results)

combination sum
def combinationSum(candidates, target):
    res = []
    candidates.sort()
    self.dfs(candidates, target, 0, [], res)
    return res

def dfs(self, nums, target, index, path, res):
    if target < 0:
        return  # backtracking
   
if target == 0:
        res.append(path)
        return
    for
i in xrange(index, len(nums)):
        self.dfs(nums, target-nums[i], i, path+[nums[i]], res)

    return result

转载于:https://www.cnblogs.com/ffeng0312/p/9739056.html

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