leetcode Copy List with Random Pointer

本文介绍了一种复杂链表结构的深拷贝方法,该链表每个节点包含一个额外的随机指针。文章详细展示了如何通过一次遍历实现节点复制,并通过第二次遍历来设置随机指针,最后拆分原始链表和拷贝链表。

A linked list is given such that each node contains an additional random pointer which could point to any node in the list or null.

Return a deep copy of the list.


Take advantage of the original list, but there is a WA code. Don't break the structure after allocate the random pointers. 

/**
 * Definition for singly-linked list with a random pointer.
 * struct RandomListNode {
 *     int label;
 *     RandomListNode *next, *random;
 *     RandomListNode(int x) : label(x), next(NULL), random(NULL) {}
 * };
 */
class Solution {
 public:
  RandomListNode *copyRandomList(RandomListNode *head) {
    if (!head)
      return NULL;
    RandomListNode *cur = head, *curcopy = NULL, *next = NULL, *res =NULL, *dummy = new RandomListNode(-1), *l2 = dummy; 
    while (cur != NULL) {
      next = cur->next;
      cur->next = new RandomListNode(cur->label);
      cur->next->next = next;
      cur = next;
    }
    cur = head;
    while (cur != NULL) {
      next = cur->next->next;
      l2->next = cur->next;
      l2 = l2->next;
      if (cur->random)
        l2->random = cur->random->next;
      cur->next = next;
      cur = next;
    }
    res = dummy->next;
    delete dummy;
    return res;
  }
};

The right code is :


/**
 * Definition for singly-linked list with a random pointer.
 * struct RandomListNode {
 *     int label;
 *     RandomListNode *next, *random;
 *     RandomListNode(int x) : label(x), next(NULL), random(NULL) {}
 * };
 */
class Solution {
 public:
  RandomListNode *copyRandomList(RandomListNode *head) {
    if (!head)
      return NULL;
    RandomListNode *cur = head, *curcopy = NULL, *next = NULL, *res =NULL, *dummy = new RandomListNode(-1), *l2 = dummy; 
    while (cur != NULL) {
      next = cur->next;
      cur->next = new RandomListNode(cur->label);
      cur->next->next = next;
      cur = next;
    }
    cur = head;
    while (cur != NULL) {
      if (cur->random)
        cur->next->random = cur->random->next;
      cur = cur->next->next;
    }
    cur = head;
    while (cur != NULL) {
      next = cur->next->next;
      l2->next = cur->next;
      l2 = l2->next;
      cur->next = next;
      cur = next;
    }
    res = dummy->next;
    delete dummy;
    return res;
  }
};


内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值