Welcome to our deep dive into the practical application of Stack data structures for solving algorithmically complex problems. Today, we will explore how a Stack, a Last-In-First-Out (LIFO) data structure, can come in handy for solving seemingly difficult computational challenges. We will focus on two problems that you can face during the technical interviews or in the real-work coding!
Let's start with our first problem. Imagine you have a list of integers, and your task is to determine the preceding smaller value for every number on the list. If a smaller previous element does not exist, you have to return -1.
Problem 1: Problem Actualization
Now, let's give this problem some context for better understanding its real-world application. Imagine you are working in finance, analyzing historical stock prices. For each day, you would like to know the previous day when the price was lower than the current price. This situation is a perfect instance where our problem comes into play, and solving it would make your everyday job a lot easier.
Problem 1: Naive Approach
While approaching this problem, your initial heuristic might lead you down the path of comparing each number with all its previous numbers. While this method does offer a solution, it's not an efficient one. Would you guess it's time complexity? Exactly, it is ! As the scale increases, this approach generates a lot of unnecessary computations, rendering it inefficient for larger data sets.
