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< title > 6.2. 哈希冲突处理 - Hello 算法< / title >
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6.2. 哈希冲突处理
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0. 写在前面
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0. 写在前面
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0.1. 关于本书
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0.2. 如何使用本书
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0.3. 编程环境安装
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0.4. 一起参与创作
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1. 引言
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1. 引言
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1.1. 算法无处不在
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1.2. 算法是什么
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2. 计算复杂度
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2. 计算复杂度
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_computational_complexity/performance_evaluation/" class = "md-nav__link" >
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2.1. 算法效率评估
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_computational_complexity/time_complexity/" class = "md-nav__link" >
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2.2. 时间复杂度
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_computational_complexity/space_complexity/" class = "md-nav__link" >
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2.3. 空间复杂度
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_computational_complexity/space_time_tradeoff/" class = "md-nav__link" >
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2.4. 权衡时间与空间
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_computational_complexity/summary/" class = "md-nav__link" >
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2.5. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_4" >
< label class = "md-nav__link" for = "__nav_4" id = "__nav_4_label" tabindex = "0" >
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3. 数据结构简介
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_4_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_4" >
< span class = "md-nav__icon md-icon" > < / span >
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3. 数据结构简介
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_data_structure/data_and_memory/" class = "md-nav__link" >
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3.1. 数据与内存
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_data_structure/classification_of_data_structure/" class = "md-nav__link" >
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3.2. 数据结构分类
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_data_structure/summary/" class = "md-nav__link" >
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3.3. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_5" >
< label class = "md-nav__link" for = "__nav_5" id = "__nav_5_label" tabindex = "0" >
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4. 数组与链表
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_5_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_5" >
< span class = "md-nav__icon md-icon" > < / span >
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4. 数组与链表
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_array_and_linkedlist/array/" class = "md-nav__link" >
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4.1. 数组( Array)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_array_and_linkedlist/linked_list/" class = "md-nav__link" >
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4.2. 链表( Linked List)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_array_and_linkedlist/list/" class = "md-nav__link" >
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4.3. 列表( List)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_array_and_linkedlist/summary/" class = "md-nav__link" >
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4.4. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_6" >
< label class = "md-nav__link" for = "__nav_6" id = "__nav_6_label" tabindex = "0" >
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5. 栈与队列
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_6_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_6" >
< span class = "md-nav__icon md-icon" > < / span >
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5. 栈与队列
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_stack_and_queue/stack/" class = "md-nav__link" >
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5.1. 栈( Stack)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_stack_and_queue/queue/" class = "md-nav__link" >
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5.2. 队列( Queue)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_stack_and_queue/deque/" class = "md-nav__link" >
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5.3. 双向队列( Deque)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_stack_and_queue/summary/" class = "md-nav__link" >
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5.4. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_7" checked >
< label class = "md-nav__link" for = "__nav_7" id = "__nav_7_label" tabindex = "0" >
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6. 散列表
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_7_label" aria-expanded = "true" >
< label class = "md-nav__title" for = "__nav_7" >
< span class = "md-nav__icon md-icon" > < / span >
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6. 散列表
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../hash_map/" class = "md-nav__link" >
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6.1. 哈希表( Hash Map)
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< / a >
< / li >
< li class = "md-nav__item md-nav__item--active" >
< input class = "md-nav__toggle md-toggle" type = "checkbox" id = "__toc" >
< label class = "md-nav__link md-nav__link--active" for = "__toc" >
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6.2. 哈希冲突处理
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< a href = "./" class = "md-nav__link md-nav__link--active" >
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6.2. 哈希冲突处理
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< / a >
< nav class = "md-nav md-nav--secondary" aria-label = "目录" >
< label class = "md-nav__title" for = "__toc" >
< span class = "md-nav__icon md-icon" > < / span >
目录
< / label >
< ul class = "md-nav__list" data-md-component = "toc" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "#621" class = "md-nav__link" >
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6.2.1. 哈希表扩容
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#622" class = "md-nav__link" >
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6.2.2. 链式地址
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#623" class = "md-nav__link" >
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6.2.3. 开放寻址
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< / a >
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< nav class = "md-nav" aria-label = "6.2.3. 开放寻址" >
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< ul class = "md-nav__list" >
< li class = "md-nav__item" >
< a href = "#_1" class = "md-nav__link" >
线性探测
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#_2" class = "md-nav__link" >
多次哈希
< / a >
< / li >
< / ul >
< / nav >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item" >
< a href = "../summary/" class = "md-nav__link" >
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6.3. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_8" >
< label class = "md-nav__link" for = "__nav_8" id = "__nav_8_label" tabindex = "0" >
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7. 二叉树
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_8_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_8" >
< span class = "md-nav__icon md-icon" > < / span >
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7. 二叉树
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_tree/binary_tree/" class = "md-nav__link" >
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7.1. 二叉树( Binary Tree)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_tree/binary_tree_traversal/" class = "md-nav__link" >
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7.2. 二叉树遍历
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_tree/binary_search_tree/" class = "md-nav__link" >
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7.3. 二叉搜索树
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_tree/avl_tree/" class = "md-nav__link" >
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7.4. AVL 树 *
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_tree/summary/" class = "md-nav__link" >
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7.5. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_9" >
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< label class = "md-nav__link" for = "__nav_9" id = "__nav_9_label" tabindex = "0" >
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8. 堆
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_9_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_9" >
< span class = "md-nav__icon md-icon" > < / span >
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8. 堆
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_heap/heap/" class = "md-nav__link" >
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8.1. 堆( Heap)
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< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../../chapter_heap/build_heap/" class = "md-nav__link" >
8.2. 建堆操作 *
< / a >
< / li >
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< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_10" >
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< label class = "md-nav__link" for = "__nav_10" id = "__nav_10_label" tabindex = "0" >
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9. 图
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_10_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_10" >
< span class = "md-nav__icon md-icon" > < / span >
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9. 图
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_graph/graph/" class = "md-nav__link" >
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9.1. 图( Graph)
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_graph/graph_operations/" class = "md-nav__link" >
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9.2. 图基础操作
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< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../../chapter_graph/graph_traversal/" class = "md-nav__link" >
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9.3. 图的遍历
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< / a >
< / li >
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< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_11" >
< label class = "md-nav__link" for = "__nav_11" id = "__nav_11_label" tabindex = "0" >
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10. 查找算法
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_11_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_11" >
< span class = "md-nav__icon md-icon" > < / span >
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10. 查找算法
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_searching/linear_search/" class = "md-nav__link" >
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10.1. 线性查找
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_searching/binary_search/" class = "md-nav__link" >
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10.2. 二分查找
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_searching/hashing_search/" class = "md-nav__link" >
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10.3. 哈希查找
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_searching/summary/" class = "md-nav__link" >
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10.4. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_12" >
< label class = "md-nav__link" for = "__nav_12" id = "__nav_12_label" tabindex = "0" >
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11. 排序算法
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_12_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_12" >
< span class = "md-nav__icon md-icon" > < / span >
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11. 排序算法
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/intro_to_sort/" class = "md-nav__link" >
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11.1. 排序简介
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/bubble_sort/" class = "md-nav__link" >
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11.2. 冒泡排序
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/insertion_sort/" class = "md-nav__link" >
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11.3. 插入排序
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/quick_sort/" class = "md-nav__link" >
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11.4. 快速排序
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/merge_sort/" class = "md-nav__link" >
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11.5. 归并排序
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../chapter_sorting/summary/" class = "md-nav__link" >
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11.6. 小结
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< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_13" >
< div class = "md-nav__link md-nav__link--index " >
< a href = "../../chapter_reference/" > 参考文献< / a >
< / div >
< nav class = "md-nav" data-md-level = "1" aria-labelledby = "__nav_13_label" aria-expanded = "false" >
< label class = "md-nav__title" for = "__nav_13" >
< span class = "md-nav__icon md-icon" > < / span >
参考文献
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6.2.1. 哈希表扩容
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6.2.2. 链式地址
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6.2.3. 开放寻址
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线性探测
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多次哈希
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< h1 id = "62" > 6.2. 哈希冲突< a class = "headerlink" href = "#62" title = "Permanent link" > ¶ < / a > < / h1 >
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< p > 理想情况下,哈希函数应该为每个输入产生唯一的输出,使得 key 和 value 一一对应。而实际上,往往存在向哈希函数输入不同的 key 而产生相同输出的情况,这种情况被称为「哈希冲突 Hash Collision」。哈希冲突会导致查询结果错误, 从而严重影响哈希表的可用性。< / p >
< p > 那么,为什么会出现哈希冲突呢?本质上看,< strong > 由于哈希函数的输入空间往往远大于输出空间< / strong > ,因此不可避免地会出现多个输入产生相同输出的情况,即为哈希冲突。比如,输入空间是全体整数,输出空间是一个固定大小的桶(数组)的索引范围,那么必定会有多个整数同时映射到一个桶索引。< / p >
< p > 为了缓解哈希冲突,一方面,我们可以通过「哈希表扩容」来减小冲突概率。极端情况下,当输入空间和输出空间大小相等时,哈希表就等价于数组了,可谓“大力出奇迹”。< / p >
< p > 另一方面,< strong > 考虑通过优化数据结构以缓解哈希冲突< / strong > ,常见的方法有「链式地址」和「开放寻址」。< / p >
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< h2 id = "621" > 6.2.1. 哈希表扩容< a class = "headerlink" href = "#621" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 「负载因子 Load Factor」定义为 < strong > 哈希表中元素数量除以桶槽数量(即数组大小)< / strong > ,代表哈希冲突的严重程度。< / p >
< p > < strong > 负载因子常用作哈希表扩容的触发条件< / strong > 。比如在 Java 中,当负载因子 < span class = "arithmatex" > \(> 0.75\)< / span > 时则触发扩容,将 HashMap 大小扩充至原先的 < span class = "arithmatex" > \(2\)< / span > 倍。< / p >
< p > 与数组扩容类似,< strong > 哈希表扩容操作的开销很大< / strong > ,因为需要将所有键值对从原哈希表依次移动至新哈希表。< / p >
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< h2 id = "622" > 6.2.2. 链式地址< a class = "headerlink" href = "#622" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 在原始哈希表中,桶内的每个地址只能存储一个元素(即键值对)。< strong > 考虑将单个元素转化成一个链表,将所有冲突元素都存储在一个链表中< / strong > 。< / p >
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< p > < img alt = "链式地址" src = "../hash_collision.assets/hash_collision_chaining.png" / > < / p >
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< p align = "center" > Fig. 链式地址 < / p >
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< p > 链式地址下,哈希表操作方法为:< / p >
< ul >
< li > < strong > 查询元素< / strong > :先将 key 输入到哈希函数得到桶内索引,即可访问链表头结点,再通过遍历链表查找对应 value 。< / li >
< li > < strong > 添加元素< / strong > :先通过哈希函数访问链表头部,再将结点(即键值对)添加到链表头部即可。< / li >
< li > < strong > 删除元素< / strong > :同样先根据哈希函数结果访问链表头部,再遍历链表查找对应结点,删除之即可。< / li >
< / ul >
< p > 链式地址虽然解决了哈希冲突问题,但仍存在局限性,包括:< / p >
< ul >
< li > < strong > 占用空间变大< / strong > ,因为链表或二叉树包含结点指针,相比于数组更加耗费内存空间;< / li >
< li > < strong > 查询效率降低< / strong > ,因为需要线性遍历链表来查找对应元素;< / li >
< / ul >
< p > 为了缓解时间效率问题,< strong > 可以把「链表」转化为「AVL 树」或「红黑树」< / strong > ,将查询操作的时间复杂度优化至 < span class = "arithmatex" > \(O(\log n)\)< / span > 。< / p >
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< h2 id = "623" > 6.2.3. 开放寻址< a class = "headerlink" href = "#623" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 「开放寻址」不引入额外数据结构,而是通过“多次探测”来解决哈希冲突。根据探测方法的不同,主要分为 < strong > 线性探测、平方探测、多次哈希< / strong > 。< / p >
< h3 id = "_1" > 线性探测< a class = "headerlink" href = "#_1" title = "Permanent link" > ¶ < / a > < / h3 >
< p > 「线性探测」使用固定步长的线性查找来解决哈希冲突。< / p >
< p > < strong > 插入元素< / strong > :如果出现哈希冲突,则从冲突位置向后线性遍历(步长一般取 1 ),直到找到一个空位,则将元素插入到该空位中。< / p >
< p > < strong > 查找元素< / strong > :若出现哈希冲突,则使用相同步长执行线性查找,会遇到两种情况:< / p >
< ol >
< li > 找到对应元素,返回 value 即可;< / li >
< li > 若遇到空位,则说明查找键值对不在哈希表中;< / li >
< / ol >
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< p > < img alt = "线性探测" src = "../hash_collision.assets/hash_collision_linear_probing.png" / > < / p >
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< p align = "center" > Fig. 线性探测 < / p >
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< p > 线性探测存在以下缺陷:< / p >
< ul >
< li > < strong > 不能直接删除元素< / strong > 。删除元素会导致桶内出现一个空位,在查找其他元素时,该空位有可能导致程序认为元素不存在(即上述第 < code > 2.< / code > 种情况)。因此需要借助一个标志位来标记删除元素。< / li >
< li > < strong > 容易产生聚集< / strong > 。桶内被占用的连续位置越长,这些连续位置发生哈希冲突的可能性越大,从而进一步促进这一位置的“聚堆生长”,最终导致增删查改操作效率的劣化。< / li >
< / ul >
< h3 id = "_2" > 多次哈希< a class = "headerlink" href = "#_2" title = "Permanent link" > ¶ < / a > < / h3 >
< p > 顾名思义,「多次哈希」的思路是使用多个哈希函数 < span class = "arithmatex" > \(f_1(x)\)< / span > , < span class = "arithmatex" > \(f_2(x)\)< / span > , < span class = "arithmatex" > \(f_3(x)\)< / span > , < span class = "arithmatex" > \(\cdots\)< / span > 进行探测。< / p >
< p > < strong > 插入元素< / strong > :若哈希函数 < span class = "arithmatex" > \(f_1(x)\)< / span > 出现冲突,则尝试 < span class = "arithmatex" > \(f_2(x)\)< / span > ,以此类推……直到找到空位后插入元素。< / p >
< p > < strong > 查找元素< / strong > :以相同的哈希函数顺序查找,存在两种情况:< / p >
< ol >
< li > 找到目标元素,则返回之;< / li >
< li > 到空位或已尝试所有哈希函数,说明哈希表中无此元素;< / li >
< / ol >
< p > 相比于「线性探测」,「多次哈希」方法更不容易产生聚集,代价是多个哈希函数增加了额外计算量。< / p >
< div class = "admonition note" >
< p class = "admonition-title" > 工业界方案< / p >
< p > Java 采用「链式地址」。在 JDK 1.8 之后, HashMap 内数组长度大于 64 时,长度大于 8 的链表会被转化为「红黑树」,以提升查找性能。< / p >
< p > Python 采用「开放寻址」。字典 dict 使用伪随机数进行探测。 < / p >
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