Comet OJ - Contest #6 双倍快乐

本文介绍了一种使用动态规划(DP)算法解决双序列最大值问题的方法。通过构建二维DP数组,实现了对两个序列中任意位置的最大值求解,确保了每个状态转移方程的有效性和正确性。该算法在每一步都考虑了当前元素是否可以加入到已有的最大值中,从而达到全局最优解。

https://www.cometoj.com/contest/48/problem/B

思路:dp[i][j] 表示:一段i结尾,一段j结尾最大值;

#include <iostream>
#include <cstring>
#include <algorithm>
#include <cstdio>
#include <queue>
#include <map>
#include <set>
#include <stack>
using namespace std;

#define sfi(x) scanf("%d",&x)
#define sfc(x) scanf("%c",x)
#define sfl(x) scanf("%lld",&x)
#define sfs(x) scanf("%s",x)

#define rint register int
#define pb push_back
#define fl() printf("flag!\n")
#define INF 0x3f3f3f3f
#define ll long long
#define mem(x,y) memset(x,y,sizeof(x))
#define FAST_IO ios::sync_with_stdio(false);cin.tie(0);cout.tie(0)

const int maxn=5e2+9;

int a[maxn];
ll dp[maxn][maxn];
int main()
{
    //FAST_IO;
    //freopen("input.txt","r",stdin);

    int n;
    cin>>n;
    for(int i=1;i<=n;i++)
    {
        cin>>a[i];
    }

    for(int k=1;k<=n;k++)
    {
        for(int i=0;i<k;i++)
        {
            for(int j=0;j<k;j++)
            {
                if(a[i]<=a[k])
                {
                    dp[k][j]=max(dp[k][j],dp[i][j]+a[k]);
                }
                if(a[j]<=a[k])
                {
                    dp[i][k]=max(dp[i][j]+a[k],dp[i][k]);
                }
            }
        }
    }

    ll ans=0;
    for(int i=1;i<=n;i++)
    {
        for(int j=1;j<=n;j++)
        {
            ans=max(ans,dp[i][j]);
        }
    }

    cout<<ans<<endl;

    //cout<<ans+max(Max,Maxx)<<endl;

    return 0;
}

 

# YOLOv5 🚀 requirements # Usage: pip install -r requirements.txt # Base ------------------------------------------------------------------------ gitpython ipython # interactive notebook matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.1 Pillow>=7.1.2 psutil # system resources PyYAML>=5.3.1 requests>=2.23.0 scipy>=1.4.1 thop>=0.1.1 # FLOPs computation torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended) torchvision>=0.8.1 tqdm>=4.64.0 # protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 # Logging --------------------------------------------------------------------- tensorboard>=2.4.1 # clearml>=1.2.0 # comet # Plotting -------------------------------------------------------------------- pandas>=1.1.4 seaborn>=0.11.0 # Export ---------------------------------------------------------------------- # coremltools>=6.0 # CoreML export # onnx>=1.9.0 # ONNX export # onnx-simplifier>=0.4.1 # ONNX simplifier # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export # scikit-learn<=1.1.2 # CoreML quantization # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) # tensorflowjs>=3.9.0 # TF.js export # openvino-dev # OpenVINO export # Deploy ---------------------------------------------------------------------- # tritonclient[all]~=2.24.0 # Extras ---------------------------------------------------------------------- # mss # screenshots # albumentations>=1.0.3 # pycocotools>=2.0 # COCO mAP # roboflow # ultralytics # HUB https://hub.ultralytics.com
最新发布
05-25
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