Codeforces 631E Product Sum 斜率优化

本文详细解析了如何通过斜率优化技巧解决动态规划问题,特别关注于如何将问题转化为斜率优化形式,以及如何确定最优解。通过实例代码,展示了算法的具体实现过程,包括关键的计算函数和主流程。

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我们先把问题分成两部分, 一部分是把元素往前移, 另一部分是把元素往后移。对于一个 i 后的一个位置, 我们考虑前面哪个移到这里来最优。

我们设最优值为val,   val = max(a[ j ] * (i - j) - (sum[ i ] - sum[ j ]) 我们能发现这个能转换成斜率优化的形式如果 j 比 k 更优且 j > k 我们能得到, 

((j * a[ j ] - sum[ j ])  - (k * a[ k ] - sum[ k ]))  < i *  (a[ j ] - a[ k ]) ,这时候我们发现(a[ j ] - a[ k ])的符号不知道, 因为 a 不是单调的。 

是我们能发现有用的 a 一定是单调递增的, 我们考虑相邻的情况,如果前面的a大, 那么它和它后一个交换肯定变优。 这样就能斜率优化啦,

反过来的情况也是一样的。

#include<bits/stdc++.h>
#define LL long long
#define fi first
#define se second
#define mk make_pair
#define PLL pair<LL, LL>
#define PLI pair<LL, int>
#define PII pair<int, int>
#define SZ(x) ((int)x.size())
#define ull unsigned long long

using namespace std;

const int N = 2e5 + 7;
const int inf = 0x3f3f3f3f;
const LL INF = 0x3f3f3f3f3f3f3f3f;
const int mod = 1e9 + 7;
const double eps = 1e-8;
const double PI = acos(-1);

int n, head = 1, rear, que[N];
LL a[N], sum[N], ans;

inline double calc(int k, int j) {
    return (((double)j * a[j] - sum[j]) - ((double)k * a[k] - sum[k])) / (a[j] - a[k]);
}

int main() {
//    freopen("text.in", "r", stdin);
    scanf("%d", &n);
    for(int i = 1; i <= n; i++) {
        scanf("%lld", &a[i]);
        ans += i * a[i];
        sum[i] = sum[i - 1] + a[i];
    }
    LL tmp = ans;
    for(int i = 1; i <= n; i++) {
        while(rear - head + 1 >= 2 && calc(que[head], que[head + 1]) <= i) head++;
        if(head <= rear) {
            int who = que[head];
            ans = max(ans, tmp + (i - who) * a[who] - sum[i] + sum[who]);
        }
        if(head > rear || (head <= rear && a[i] > a[que[rear]])) {
            while(rear - head + 1 >= 2 && calc(que[rear - 1], que[rear]) > calc(que[rear], i)) rear--;
            que[++rear] = i;
        }
    }

    rear = 0; head = 1;
    reverse(a + 1, a + 1 + n);
    for(int i = 1; i <= n; i++) sum[i] = sum[i - 1] + a[i];
    for(int i = 1; i <= n; i++) {
        while(rear - head + 1 >= 2 && calc(que[head], que[head + 1]) <= i) head++;
        if(head <= rear) {
            int who = que[head];
            ans = max(ans, tmp + sum[i] - sum[who] - (i - who) * a[who]);
        }
        if(head > rear || (head <= rear && a[i] < a[que[rear]])) {
            while(rear - head + 1 >= 2 && calc(que[rear - 1], que[rear]) > calc(que[rear], i)) rear--;
            que[++rear] = i;
        }
    }
    printf("%lld\n", ans);
    return 0;
}

/*
*/

 

转载于:https://www.cnblogs.com/CJLHY/p/10471099.html

### Codeforces 887E Problem Solution and Discussion The problem **887E - The Great Game** on Codeforces involves a strategic game between two players who take turns to perform operations under specific rules. To tackle this challenge effectively, understanding both dynamic programming (DP) techniques and bitwise manipulation is crucial. #### Dynamic Programming Approach One effective method to approach this problem utilizes DP with memoization. By defining `dp[i][j]` as the optimal result when starting from state `(i,j)` where `i` represents current position and `j` indicates some status flag related to previous moves: ```cpp #include <bits/stdc++.h> using namespace std; const int MAXN = ...; // Define based on constraints int dp[MAXN][2]; // Function to calculate minimum steps using top-down DP int minSteps(int pos, bool prevMoveType) { if (pos >= N) return 0; if (dp[pos][prevMoveType] != -1) return dp[pos][prevMoveType]; int res = INT_MAX; // Try all possible next positions and update 'res' for (...) { /* Logic here */ } dp[pos][prevMoveType] = res; return res; } ``` This code snippet outlines how one might structure a solution involving recursive calls combined with caching results through an array named `dp`. #### Bitwise Operations Insight Another critical aspect lies within efficiently handling large integers via bitwise operators instead of arithmetic ones whenever applicable. This optimization can significantly reduce computation time especially given tight limits often found in competitive coding challenges like those hosted by platforms such as Codeforces[^1]. For detailed discussions about similar problems or more insights into solving strategies specifically tailored towards contest preparation, visiting forums dedicated to algorithmic contests would be beneficial. Websites associated directly with Codeforces offer rich resources including editorials written after each round which provide comprehensive explanations alongside alternative approaches taken by successful contestants during live events. --related questions-- 1. What are common pitfalls encountered while implementing dynamic programming solutions? 2. How does bit manipulation improve performance in algorithms dealing with integer values? 3. Can you recommend any online communities focused on discussing competitive programming tactics? 4. Are there particular patterns that frequently appear across different levels of difficulty within Codeforces contests?
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