Codeforces 722E [DP]

本文介绍了一种使用动态规划算法解决特定网格图中路径计数问题的方法。考虑到电池衰减的不同状态,通过优化DP过程来减少计算复杂度。

Solution

以前做过一道题是这样的

  • 给定nm的网格图和k个点,求不定过某些点从(1,1)走到(n,m)的方案数。

这个的话直接O(k2)DP就好了。
这道题也差不多,因为电池的衰减只有O(log2s)种,所以DP时记录一下当前经过的点的个数就好了。

#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <algorithm>
using namespace std;

const int N = 101010;
const int M = 2020;
const int MOD = 1000000007;
typedef long long ll;

inline char get(void) {
    static char buf[100000], *S = buf, *T = buf;
    if (S == T) {
        T = (S = buf) + fread(buf, 1, 100000, stdin);
        if (S == T) return EOF;
    }
    return *S++;
}
inline void read(int &x) {
    static char c; x = 0;
    for (c = get(); c < '0' || c > '9'; c = get());
    for (; c >= '0' && c <= '9'; c = get()) x = x * 10 + c - '0';
}

int n, m, k, s, lim, ans;
struct Point {
    int x, y;
    Point (int _x = 0, int _y = 0):x(_x), y(_y) {}
    inline friend bool operator <(const Point &a, const Point &b) {
        return a.x == b.x ? a.y < b.y : a.x < b.x;
    }
};
Point a[M];
int fac[N << 1], inv[N << 1];
int dp[N][100];

inline int Pow(int a, int b) {
    int c = 1;
    while (b) {
        if (b & 1) c = (ll)c * a % MOD;
        b >>= 1; a = (ll)a * a % MOD;
    }
    return c;
}
inline int Inv(int x) {
    return Pow(x, MOD - 2);
}
inline int C(int n, int m) {
    return (ll)fac[n] * inv[m] % MOD * inv[n - m] % MOD;
}
inline int Calc(int n, int m) {
    return C(n + m, n);
}

int main(void) {
    read(n); read(m); read(k); read(s);
    inv[1] = 1; lim = 25;
    for (int i = 2; i <= 200000; i++)
        inv[i] = (ll)(MOD - MOD / i) * inv[MOD % i] % MOD;
    fac[0] = inv[0] = 1;
    for (int i = 1; i <= 200000; i++) {
        fac[i] = (ll)fac[i - 1] * i % MOD;
        inv[i] = (ll)inv[i - 1] * inv[i] % MOD;
    }
    for (int i = 1; i <= k; i++) {
        read(a[i].x); read(a[i].y);
    }
    sort(a + 1, a + k + 1);
    if (a[k].x == n && a[k].y == m) s -= s / 2;
    else a[++k] = Point(n, m);
    if (a[1].x != 1 || a[1].y != 1) {
        s *= 2; a[++k] = Point(1, 1);
    }
    sort(a + 1, a + k + 1);
    dp[1][0] = 1;
    for (int i = 2; i <= k; i++) {
        dp[i][1] = Calc(a[i].x - 1, a[i].y - 1);
        for (int d = 2; d <= lim; d++) {
            for (int j = 1; j < i; j++) {
                if (a[j].y > a[i].y) continue;
                dp[i][d] += (ll)dp[j][d - 1] * Calc(a[i].x - a[j].x, a[i].y - a[j].y) % MOD;
                dp[i][d] -= (ll)dp[j][d] * Calc(a[i].x - a[j].x, a[i].y - a[j].y) % MOD;
                dp[i][d] = (dp[i][d] % MOD + MOD) % MOD;
            }
        }
    }
    ans = 0;
    for (int i = 1; i <= lim; i++) {
        s -= s / 2;
        ans = ans + (ll)(dp[k][i] - dp[k][i + 1] + MOD) * s % MOD;
        ans %= MOD;
    }
    cout << (ll)ans * Inv(Calc(n - 1, m - 1)) % MOD << endl;
    return 0;
}
### 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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