Search in Rotated Sorted Array 旋转数组里查找数 @LeetCode

本文介绍了一种在旋转过的有序数组中查找特定元素的方法,并详细解释了如何利用二分查找法的变种来解决这一问题。文章还探讨了当数组存在重复元素时的处理方式,并给出了具体的实现代码。

二分法的变型题

FollowUp 是如果数组里有重复元素怎么办?

其实没所谓。

http://blog.youkuaiyun.com/fightforyourdream/article/details/16857291


package Level4;

/**
 * Search in Rotated Sorted Array
 * 
 * Suppose a sorted array is rotated at some pivot unknown to you beforehand.
 * 
 * (i.e., 0 1 2 4 5 6 7 might become 4 5 6 7 0 1 2).
 * 
 * You are given a target value to search. If found in the array return its
 * index, otherwise return -1.
 * 
 * You may assume no duplicate exists in the array.
 * 
 */
public class S33 {

	public static void main(String[] args) {
		int[] A = {2, 3, 4, 5, 6, 0, 1};
		System.out.println(search(A, 0));
	}

	public static int search(int[] A, int target) {
		return rec(A, target, 0, A.length-1);
	}
	
	// 递归查找
	public static int rec(int[] A, int target, int low, int high){
		if(low > high){				// 没找到的情况
			return -1;
		}
		
		int mid = low + (high-low)/2;
		if(A[mid] == target){		// 找到了
			return mid;
		}
		
		int res = rec(A, target, low, mid-1);		// 在左侧查找
		if(res == -1){			// 如果左侧没找到,继续在右侧查找
			res = rec(A, target, mid+1, high);
		}
		
		return res;
	}

}



解法就是直接分别对左半侧和右半侧搜索,用递归实现

public class Solution {
    public int search(int[] A, int target) {
        return rec(A, target, 0, A.length-1);
    }
    
    public int rec(int[] A, int target, int low, int high){
        if(low > high){
            return -1;
        }
        
        int mid = low + (high-low)/2;
        if(A[mid] == target){
            return mid;
        }
        int pos = rec(A, target, mid+1, high);
        if(pos != -1){
            return pos;
        }else{
            return rec(A, target, low, mid-1);
        }
    }
}


I have updated the problem description to assume that there exists no duplicate in the array. Some readers have noted that the below code does not work for input with duplicates. For example, for input “1 2 1 1 1 1″, the binary search method below would not work, as there is no way to know if an element exists in the array without going through each element one by one.

At first look, we know that we can do a linear search in O(n) time. But linear search does not need the elements to be sorted in any way.

First, we know that it is a sorted array that’s been rotated. Although we do not know where the rotation pivot is, there is a property we can take advantage of. Here, we make an observation that a rotated array can be classified as two sub-array that is sorted (i.e., 4 5 6 7 0 1 2 consists of two sub-arrays 4 5 6 7 and 0 1 2.

Do not jump to conclusion that we need to first find the location of the pivot and then do binary search on both sub-arrays. Although this can be done in O(lg n) time, this is not necessary and is more complicated.

In fact, we don’t need to know where the pivot is. Look at the middle element (7). Compare it with the left most (4) and right most element (2). The left most element (4) is less than (7). This gives us valuable information — All elements in the bottom half must be in strictly increasing order. Therefore, if the key we are looking for is between 4 and 7, we eliminate the upper half; if not, we eliminate the bottom half.

When left index is greater than right index, we have to stop searching as the key we are finding is not in the array.

Since we reduce the search space by half each time, the complexity must be in the order of O(lg n). It is similar to binary search but is somehow modified for this problem. In fact, this is more general than binary search, as it works for both rotated and non-rotated arrays.

二分法的·精髓在于只要能做到每次扔掉一半

int rotated_binary_search(int A[], int N, int key) {
  int L = 0;
  int R = N - 1;
 
  while (L <= R) {
    // Avoid overflow, same as M=(L+R)/2
    int M = L + ((R - L) / 2);
    if (A[M] == key) return M;
 
    // the bottom half is sorted
    if (A[L] <= A[M]) {
      if (A[L] <= key && key < A[M])
        R = M - 1;
      else
        L = M + 1;
    }
    // the upper half is sorted
    else {
      if (A[M] < key && key <= A[R])
        L = M + 1;
      else 
        R = M - 1;
    }
  }
  return -1;
}


扩展:

如何找pivot点:相当于找到最小的那个点:把中点和右边的点比较

int FindSortedArrayRotation(int A[], int N) {
  int L = 0;
  int R = N - 1;
  
  while (A[L] > A[R]) {
    int M = L + (R - L) / 2;
    if (A[M] > A[R])
      L = M + 1;
    else
      R = M;
  }
  return L;
}




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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