6-93 Linked List Implementation of Stacks

本文介绍了栈作为后进先出(LIFO)数据结构的概念,并使用单链表来实现栈。栈的基本操作包括push(插入)、pop(删除最近插入的元素)和top(查看顶部元素)。在空栈上执行pop或top通常被视为错误,而push时空间不足则是实现错误。通过在链表头部插入和删除元素,实现了栈的功能。

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A stack is a list with the restriction that inserts and deletes can be performed in only one position, namely the end of ofthe list called the top. The fundamental operations on a stack are push, which is equivalent to an insert, and pop, whichdeletes the most recently inserted element. The most recently inserted element can be examined prior to performing a pop by use of the top routine. A pop or top on an empty stack is generally considered an error in the stack ADT. On the other hand, running out of space when performing a push is an implementation error but not an ADT error.

Stacks are sometimes known as LIFO (last in, first out) lists. The model depicted in Figure 3.37 signifies only thatpushes are input operations and pops and tops are output. The usual operations to make

自适应链表是一种数据结构,它能够自动调整其内部节点的数量以优化存储效率,特别是在插入和删除操作频繁的情况下。传统的链表需要预先分配一定数量的节点,而自适应链表则动态地管理节点,当内存空间不足时添加新节点,当内存充足且连续时合并节点以减少空闲空间。 下面是一个简单的自适应链表(Adaptive Array List)的Python实现概述: ```python class Node: def __init__(self, value): self.value = value self.next = None self.prev = None class AdaptiveList: def __init__(self): self.head = None self.tail = None self.size = 0 self.capacity = 16 # 初始容量 def append(self, value): if not self.head: self.head = self.tail = Node(value) else: new_node = Node(value) self.tail.next = new_node new_node.prev = self.tail self.tail = new_node self.size += 1 if self.size > self.capacity * 2: # 当大小超过两倍容量时扩容 self.resize(2 * self.capacity) def resize(self, new_capacity): old_head = self.head self.head = self.tail = Node(None) # 创建新的头和尾 self.tail.next = old_head old_head.prev = self.tail for _ in range(self.size): current = old_head old_head = old_head.next self.append(current.value) # 将所有元素移动到新链表 # 使用示例 alist = AdaptiveList() alist.append(1) alist.append(2) ... ``` 在这个实现中,`append`方法会检查是否需要扩容。如果链表已满,就创建一个新的更大的链表,然后将原有链表的所有节点复制到新链表。这样,我们可以在保持高效的同时避免浪费内存。
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