题目:
Given an integer array with all positive numbers and no duplicates, find the number of possible combinations that add up to a positive integer target.
Example:
nums = [1, 2, 3] target = 4 The possible combination ways are: (1, 1, 1, 1) (1, 1, 2) (1, 2, 1) (1, 3) (2, 1, 1) (2, 2) (3, 1) Note that different sequences are counted as different combinations. Therefore the output is 7.
Follow up:
What if negative numbers are allowed in the given array?
How does it change the problem?
What limitation we need to add to the question to allow negative numbers?
思路:
一道典型的动态规划题目(是不是感觉和背包问题很像?)。我们定义dp[i]表示给定nums后,和为i的时候有多少种排列方式,这样状态转移方程就是:dp[i] = sum(dp[i - nums[j]]),其中i - nums[j] >= 0。该算法的空间复杂度是O(target),时间复杂度是O(target * n),其中n是数组的长度。
如果允许负数存在,则问题的解有可能为无穷大,例如如果数组中有元素1,-1,那么这两个数字可以出现无限次。因此为了得到有限解我们就必须要限制每个数能用的最大次数了。
代码:
class Solution {
public:
int combinationSum4(vector<int>& nums, int target) {
sort(nums.begin(), nums.end());
vector<int> dp(target + 1, 0);
dp[0] = 1;
for (int i = 1; i <= target; ++i) {
for (int j = 0; j < nums.size(); ++j) {
if (nums[j] > i) {
break;
}
dp[i] += dp[i - nums[j]];
}
}
return dp[target];
}
};
本文介绍了一道典型动态规划问题——组合求和IV的解决方法,给出了解决此问题的C++代码实现,并讨论了允许负数存在的特殊情况及解决策略。
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