You need to implement a stack that supports push, pop, and find-minimum operations, all in O(1) time complexity. Which data structure is best suited for this scenario?
Two stacks: one for the main data and one for storing elements in sorted order.
A single stack storing only the minimum element encountered so far.
A single stack where each element is a pair containing the value and the minimum value up to that point.
A binary search tree to efficiently maintain sorted data and find the minimum.
You are tasked with designing a double-ended stack using a fixed-size array. Which of the following strategies is MOST likely to result in frequent stack overflows, even when the total number of elements in the stack is significantly less than the array's capacity?
Growing the stack from both ends towards the middle of the array.
Using separate head and tail pointers that move towards each other.
Growing the stack from one end and allowing the other end to wrap around when it reaches the array boundary.
Resizing the array dynamically whenever an overflow occurs.
The stock span problem requires finding the number of consecutive days before each day with a stock price less than or equal to the current day's price. What is the time complexity of the most efficient algorithm for this problem using a stack?
O(1)
O(n)
O(n^2)
O(n log n)
Imagine you're implementing a stack with a fixed-size array. Which situation leads to a stack overflow even if the number of elements in the stack is less than the array's size?
Pushing an element when the stack pointer is at the end of the array, even if some initial array slots are empty.
Pushing an element when the stack pointer is at the middle of the array.
Popping an element when the stack pointer is at the end of the array.
Popping an element when the stack pointer is at the beginning of the array.
In a persistent stack implementation, what happens when you push a new element onto the stack?
A new stack is created with the new element, preserving the original stack.
The original stack is modified to include the new element.
The new element replaces the top element of the original stack.
An error occurs as persistent stacks are immutable.
In the largest rectangle in a histogram problem, we aim to find the rectangle with the maximum area within a given histogram. How does the stack help in efficiently determining the area of potential rectangles?
The stack maintains the areas of all previously encountered rectangles for comparison.
The stack stores the heights of the bars in increasing order, allowing for quick area calculation.
The stack is not used in the most efficient solutions to this problem.
The stack keeps track of the starting indices of potential rectangles, enabling efficient width calculation.
Which of the following scenarios is MOST likely to benefit from using a persistent stack data structure?
Implementing an undo/redo functionality in a text editor.
All of the above.
Managing function call stacks in a recursive algorithm.
Storing a history of user actions for analytics purposes.
In a persistent stack implementation using linked lists, what is the time complexity of performing a 'pop' operation on a stack with 'n' elements?
It depends on the implementation.
O(log n)
Which of these scenarios would particularly benefit from using a persistent stack?
Representing the order of web pages visited in a browser's history.
Storing a dynamically changing list of tasks in a to-do app.
Implementing undo/redo functionality in a text editor.
Which data structure is often used in conjunction with a persistent stack to efficiently manage the different versions of the stack?
Queue
Hash table
Linked list
Binary tree