Third Maximum Number

本文介绍了一种高效算法,用于从整数数组中找出第三大的数。若不存在,则返回最大值。通过使用优先队列和集合,确保了算法的时间复杂度为O(n)。

Given a non-empty array of integers, return the third maximum number in this array. If it does not exist, return the maximum number. The time complexity must be in O(n).

Example 1:

Input: [3, 2, 1]

Output: 1

Explanation: The third maximum is 1.

Example 2:

Input: [1, 2]

Output: 2

Explanation: The third maximum does not exist, so the maximum (2) is returned instead.

Example 3:

Input: [2, 2, 3, 1]

Output: 1

Explanation: Note that the third maximum here means the third maximum distinct number.
Both numbers with value 2 are both considered as second maximum.

这道题的测试用例有MIN_VALUE 这个值,因此如果想用三个变量保存前三大的值的话,这三个变量得是long型的,有点蛋疼;于是用priorityqueue做可以避免这些事情;但是这道题是不允许重复的,所以要用一个set保存结果。这样扫一遍就知道第三大是少多了。如果不存在第三大,那么要么是1 要么是2, 2的话再删除一个元素,返回最大值。

代码:

public int thirdMax(int[] nums) {
        Queue<Integer> queue = new PriorityQueue<>();
        Set<Integer> set = new HashSet<>();
        for(int num: nums){
            if(!set.contains(num)){
                set.add(num);
                queue.offer(num);
                if(queue.size()>3) queue.poll();
            }
        }
        if(queue.size()==2) queue.poll();
        return queue.peek();
    }


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,通过学优化器加速函(MOA)和学优化概率(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在函优化、图像分割等实际问题中的建模与解方法;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。
Yousef has an array a of size n . He wants to partition the array into one or more contiguous segments such that each element ai belongs to exactly one segment. A partition is called cool if, for every segment bj , all elements in bj also appear in bj+1 (if it exists). That is, every element in a segment must also be present in the segment following it. For example, if a=[1,2,2,3,1,5] , a cool partition Yousef can make is b1=[1,2] , b2=[2,3,1,5] . This is a cool partition because every element in b1 (which are 1 and 2 ) also appears in b2 . In contrast, b1=[1,2,2] , b2=[3,1,5] is not a cool partition, since 2 appears in b1 but not in b2 . Note that after partitioning the array, you do not change the order of the segments. Also, note that if an element appears several times in some segment bj , it only needs to appear at least once in bj+1 . Your task is to help Yousef by finding the maximum number of segments that make a cool partition. Input The first line of the input contains integer t (1≤t≤104 ) — the number of test cases. The first line of each test case contains an integer n (1≤n≤2⋅105 ) — the size of the array. The second line of each test case contains n integers a1,a2,…,an (1≤ai≤n ) — the elements of the array. It is guaranteed that the sum of n over all test cases doesn't exceed 2⋅105 . Output For each test case, print one integer — the maximum number of segments that make a cool partition. Example InputCopy 8 6 1 2 2 3 1 5 8 1 2 1 3 2 1 3 2 5 5 4 3 2 1 10 5 8 7 5 8 5 7 8 10 9 3 1 2 2 9 3 3 1 4 3 2 4 1 2 6 4 5 4 5 6 4 8 1 2 1 2 1 2 1 2 OutputCopy 2 3 1 3 1 3 3 4 Note The first test case is explained in the statement. We can partition it into b1=[1,2] , b2=[2,3,1,5] . It can be shown there is no other partition with more segments. In the second test case, we can partition the array into b1=[1,2] , b2=[1,3,2] , b3=[1,3,2] . The maximum number of segments is 3 . In the third test case, the only partition we can make is b1=[5,4,3,2,1]
06-09
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