【USACO10JAN】Cheese Towers S 奶酪塔 (背包dp)

一种思路奇特的做法。

看到题目容易联想到背包dp,因为看上去很像

但是我们并不知道上面有没有大奶酪。

所以我们不妨倒过来看,从上往下加奶酪。

d p ( i , 1 / 0 ) dp(i,1/0) dp(i,1/0) 表示当前从上往下的累加高度为 i i i,这之中有/无大奶酪。

显然,当我们考虑新加一个奶酪时,有:

{ d p ( i , 0 ) = max ⁡ ( d p ( i − h j , 0 ) + v j )    ( h j < K ) d p ( i , 1 ) = max ⁡ ( d p ( i − h j , 0 ) + v j )    ( h j ≥ K ) \begin{cases} dp(i,0)=\max(dp(i-h_j,0)+v_j)\ \ (h_j<K)\\ dp(i,1)=\max(dp(i-h_j,0)+v_j)\ \ (h_j\geq K) \end{cases} {dp(i,0)=max(dp(ihj,0)+vj)  (hj<K)dp(i,1)=max(dp(ihj,0)+vj)  (hjK)

然后对于 d p ( i , 1 ) dp(i,1) dp(i,1),还有:

d p ( i , 1 ) = max ⁡ ( d p ( i − 4 5 h j , 1 ) + v j ) dp(i,1)=\max(dp(i-\frac{4}{5}h_j,1)+v_j) dp(i,1)=max(dp(i54hj,1)+vj)

总的时间复杂度 O ( n t ) O(nt) O(nt),听说 O ( n 2 t ) O(n^2t) O(n2t) 也能过……

#include<bits/stdc++.h>

#define N 110
#define T 1010

using namespace std;

int n,t,k,ans;
int v[N],h[N];
int dp[T][2];

int main()
{
	scanf("%d%d%d",&n,&t,&k);
	for(int i=1;i<=n;i++)
		scanf("%d%d",&v[i],&h[i]);
	memset(dp,128,sizeof(dp));
	dp[0][0]=0;
	for(int i=0;i<=t;i++)
	{
		for(int j=1;j<=n;j++)
		{
			if(i-h[j]>=0)
			{
				if(h[j]<k) dp[i][0]=max(dp[i][0],dp[i-h[j]][0]+v[j]);
				else dp[i][1]=max(dp[i][1],dp[i-h[j]][0]+v[j]);
			}
		}
		for(int j=1;j<=n;j++)
			if(i-h[j]*4/5>=0)
				dp[i][1]=max(dp[i][1],dp[i-h[j]*4/5][1]+v[j]);
		ans=max(ans,max(dp[i][0],dp[i][1]));
	}
	printf("%d\n",ans);
	return 0;
}
### USACO 2016 January Contest Subsequences Summing to Sevens Problem Solution and Explanation In this problem from the USACO contest, one is tasked with finding the size of the largest contiguous subsequence where the sum of elements (IDs) within that subsequence is divisible by seven. The input consists of an array representing cow IDs, and the goal is to determine how many cows are part of the longest sequence meeting these criteria; if no valid sequences exist, zero should be returned. To solve this challenge efficiently without checking all possible subsequences explicitly—which would lead to poor performance—a more sophisticated approach using prefix sums modulo 7 can be applied[^1]. By maintaining a record of seen remainders when dividing cumulative totals up until each point in the list by 7 along with their earliest occurrence index, it becomes feasible to identify qualifying segments quickly whenever another instance of any remainder reappears later on during iteration through the dataset[^2]. For implementation purposes: - Initialize variables `max_length` set initially at 0 for tracking maximum length found so far. - Use dictionary or similar structure named `remainder_positions`, starting off only knowing position `-1` maps to remainder `0`. - Iterate over given numbers while updating current_sum % 7 as you go. - Check whether updated value already exists inside your tracker (`remainder_positions`). If yes, compare distance between now versus stored location against max_length variable's content—update accordingly if greater than previous best result noted down previously. - Finally add entry into mapping table linking latest encountered modulus outcome back towards its corresponding spot within enumeration process just completed successfully after loop ends normally. Below shows Python code implementing described logic effectively handling edge cases gracefully too: ```python def find_largest_subsequence_divisible_by_seven(cow_ids): max_length = 0 remainder_positions = {0: -1} current_sum = 0 for i, id_value in enumerate(cow_ids): current_sum += id_value mod_result = current_sum % 7 if mod_result not in remainder_positions: remainder_positions[mod_result] = i else: start_index = remainder_positions[mod_result] segment_size = i - start_index if segment_size > max_length: max_length = segment_size return max_length ```
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