Sicily 1033. City Road

本文介绍了一个关于城市道路中寻找最短路径数量的问题。在一座由M*N个小正方形房屋组成的城市中,考虑到新建筑可能阻碍某些街道,文章提供了一种算法来重新计算从一个桥到另一个桥的不同最短路径的数量。

1033. City Road

Constraints

Time Limit: 1 secs, Memory Limit: 32 MB

Description

Long long ago, city Old was all around the water and was divided into M*N small square houses. The city Old had only two bridges, in the southwest corner and northeast corner. It’s obvious that the citizens can have C(m+n, n) different ways with shortest length, to go from one bridge to the other.

     As the city developed, bigger buildings came out and blocked some streets. These new buildings were always large rectangles combining many small houses. Since the new buildings may affect the number of shortest ways a lot, it’s you job now to recalculate the number.

     The city has M rows and N columns of houses, M+1 horizontal streets, and N+1 vertical streets. Suppose the house in the southwest corner is (1, 1), then the house in the northeast corner can be marked as (m, n). And then each new building can be described by its southwest corner(house) and its size(number of houses) in horizontal and vertical directions.

     The picture below shows that the city Old has 5*6 houses and one 3*2 building with its southwest corner located at (2,4). The number of different shortest ways in the picture is 192.

Input

This problem has multiple test cases. In each test case, the first line is two integer m, n(m*n<=1,000,000)which indicate the size of city Old, in vertical and horizontal directions respectively. And the second line has one integer B(B<=1000) what is the number of the new buildings. After that there are B lines. Each line has four integers x, y, a, b, describing the southwest corner (x, y) , the vertical size a, and the horizontal size b of the building. The buildings may be overlapped with each other. The input is terminated by a case with m*n=0, which should not be processed.

Output

For each test case output one integer representing the number of shortest ways in a single line. This number is guaranteed to be less than 2^63.

Sample Input

5 612 4 3 20 0

Sample Output

192

Problem Source

ZSUACM Team Member

// Problem#: 1033
// Submission#: 3402870
// The source code is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
// URI: http://creativecommons.org/licenses/by-nc-sa/3.0/
// All Copyright reserved by Informatic Lab of Sun Yat-sen University
#include <stdio.h>
#include <iostream>
#include <vector>
#include <string>
#include <stack>
#include <iomanip>
#include <algorithm>
#include <queue>
#include <functional>
#include <map>
#include <string.h>
#include <math.h>
using namespace std;

const int MAX = 1000010;

int N, M, B;

bool G[MAX];
long long dp[2][MAX];

int main() {

    //std::ios::sync_with_stdio(false);

    while (1) {

        scanf("%d%d", &M, &N);
        if (!M && !N) break;

        int size = N * M;

        for (int i = 1; i <= size; i++) G[i] = false;

        scanf("%d", &B);
        for (int i = 0; i < B; i++) {
            int x, y, a, b;
            scanf("%d%d%d%d", &x, &y, &a, &b);
            a = x + a - 1;
            b = y + b - 1;
            for (int j = x; j < a; j++) {
                for (int k = y; k < b; k++) {
                    G[(j - 1) * N + k] = true;
                }
            }
        }

        int lastLine = 0, nowLine = 1;

        for (int i = 1; i <= N; i++) {
            dp[0][i] = 1;
            dp[1][i] = 0;
        }

        for (int i = 1; i <= M; i++) {
            dp[nowLine][0] = 1;
            for (int j = 1; j <= N; j++) {
                if (G[(i - 1) * N + j])
                    dp[nowLine][j] = 0;
                else 
                    dp[nowLine][j] = dp[lastLine][j] + dp[nowLine][j - 1];
            }
            nowLine = 1 - nowLine;
            lastLine = 1 - lastLine;
        }

        printf("%lld\n", dp[lastLine][N]);

    }

    return 0;
}                                 


内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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