Cloning a Stack without Using Extra Memory Using Java

Problem Statement

An example of copying a stack of integers may best be described as follows: Normally, we would need an auxiliary stack or another data structure to establish this circumstance. Of course, in this case, we do not have additional space for cloning, so we need to clone as is.

Example:

Input:

Output:

 Cloned Stack: [1, 2, 3, 4] 

Approach

To solve this problem, it's going to be helpful to use recursion to clone the stack. Recursive call stack is also a kind of temporary storage where we reverse the elements for some time, while shifting it from one stack to the other. This approach uses the following steps:

Recursive Element Removal: Recursion was used to recursive call for Popping the element in the original stack until the stack has no more elements. Each component is passed to the recursive function and stored there for a brief period.

Reinsertion After Cloning: When the bottom of that list is reached, remove the item and then push it back into the original list as well as the cloned list from the top of that list.

File Name: CloneStackWithoutExtraSpace.java

Output:

 Original Stack: [1, 2, 3, 4] Cloned Stack: [1, 2, 3, 4] 

Explanation

The cloneStack() method accepts an original stack and creates an instance of the clonedStack with which the copy will be created. That is why it utilizes a recursive helper function called cloneRecursively to achieve the cloning.

cloneRecursively starts by first checking whether the new passed stack is empty or not. It means that, in the case of no more elements to copy the method halts, all the required sequences are copied. If the list still has items, it gets the top element and saves it for later. The process of removal also recurs, every element is removed and stored until there are not any elements left on the stack.

However, when the stack is empty, the method reverses each of the steps and adds each stored element back into the original as well as the cloned stack. This approach overwrites the original stack with initial order while using clonedStack to store the exact copy of the stack with the similar order.

Edge Cases to Consider

Empty Stack: If the original stack is empty it will not produce an error, and process it will handle it properly. The cloned stack, you want to create, will also be an empty stack.

Single Element Stack: Well, if we are going to apply the algorithm with just one value for the stack, the algorithm will be fine because it will just push that single operand value to the cloned stack without any problem.

Stack with Duplicate Elements: Duplicate elements are also addressed well because every single element is cloned and pushed back inside the queue separately.

Complexity Analysis

Time Complexity: The time complexity of this approach is O(n), where n is the number of elements in the stack. Each element is pushed and popped twice, which still results in linear complexity.

Space Complexity: This solution has therefore O (1) auxiliary space complexity as no extra structures such as array and stack are employed. However, due to recursion stack, the elements will be held temporarily thus it is technically a O(n) call stack. This still works given our constraint that no space outside of the recursive call stack can be used.

Pros of Recursive Approach

This approach achieves the goal of cloning a stack without auxiliary storage structures:

Memory Efficiency: It does not generate extra stacks or arrays for holding the items.

Simplicity: The code is actually pretty simple and very readable and uses recursion to make element handling as easy as possible.

Conclusion

In this article, the author showed how cloning the stack could be done without using additional space for storing data apart from the call stack. By recursion, we appropriately reinstate the function call stack as a form of temporary storage, thus at the end, utilizing space of a cloned stack with nearly zero overhead.

This technique applies not only to the stack cloning but also explains the fact of how recursion serves more than as an extra data structure when working with reversible/reversible-like operations on collections.