486. Predict the Winner

本文介绍了一个有趣的游戏策略问题,即两个玩家轮流从数组两端选择数字,最终比较总和大小确定胜负。通过动态规划的方法,详细解释了如何预测第一个玩家是否能赢得比赛。

Given an array of scores that are non-negative integers. Player 1 picks one of the numbers from either end of the array followed by the player 2 and then player 1 and so on. Each time a player picks a number, that number will not be available for the next player. This continues until all the scores have been chosen. The player with the maximum score wins.

Given an array of scores, predict whether player 1 is the winner. You can assume each player plays to maximize his score.

Input: [1, 5, 2]
Output: False
Explanation: Initially, player 1 can choose between 1 and 2. 
If he chooses 2 (or 1), then player 2 can choose from 1 (or 2) and 5. If player 2 chooses 5, then player 1 will be left with 1 (or 2). 
So, final score of player 1 is 1 + 2 = 3, and player 2 is 5. 
Hence, player 1 will never be the winner and you need to return False.
Input: [1, 5, 233, 7]
Output: True
Explanation: Player 1 first chooses 1. Then player 2 have to choose between 5 and 7. No matter which number player 2 choose, player 1 can choose 233.
Finally, player 1 has more score (234) than player 2 (12), so you need to return True representing player1 can win.

玩家P1,P2轮流从数组两端选择一个数字,最后拿到数字的和多的那一方胜。P1先拿,判断玩家1是否能胜利。

解决:创建一个n*n的数组dp,因为是P1先拿,所以dp[0][n-1]或dp[n-1][0]就是玩家1能够获得的分数。

假设有数组nums[i: j],当玩家取nums[i],那么dp[i][j] = nums[i] + sum[i+1][j] - max(dp[i+1][j], dp[j][i+1]);

          当玩家取nums[j],那么dp[j][i] = nums[j] + sum[i][j-1] - max(dp[i][j-1], dp[j-1][i]);

最后要注意一下数组的更新顺序。

class Solution {
public:
    bool PredictTheWinner(vector<int>& nums) {
        int len = nums.size();
        vector<vector<int>> sum(len, vector<int>(len));
        for (int i=0; i<len; ++i)
            sum[i][i] = nums[i];
        for (int i=0; i<len; ++i)
            for (int j=i+1; j<len; ++j) {
                sum[i][j] = sum[i][j-1] + nums[j];
                sum[j][i] = sum[i][j];
            }
        vector<vector<int>> dp(len, vector<int>(len));
        for (int i=0; i<len; ++i)
            dp[i][i] = nums[i];
        for (int i=1; i<len; ++i) {
            for (int j=0; j<i; ++j) 
                dp[j][i] = nums[i] + sum[j][i-1] - max(dp[j][i-1], dp[i-1][j]);
            for (int j=i-1; j>=0; --j)
                dp[i][j] = nums[j] + sum[j+1][i] - max(dp[j+1][i], dp[i][j+1]);
        }
        return 2*max(dp[len-1][0], dp[0][len-1]) >= sum[len-1][0];
    }
};

 

转载于:https://www.cnblogs.com/Zzz-y/p/8157817.html

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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