When solving problems in Data Structures and Algorithms (DSA), constraints can often guide us toward the optimal approach and reduce trial and error. Constraints are typically given as part of the problem statement, and they inform us about the input size, expected solution complexity, and approach we should take to solve the problem efficiently. Understanding how to interpret these constraints is a valuable skill that can help predict the appropriate approach for a given problem.
In this blog post, we’ll explore how to predict the optimal approach for a DSA problem simply by analyzing its constraints. By recognizing the input size, value range, and time complexity requirements, you can make informed decisions about the algorithmic strategy to use. Whether you're dealing with small inputs or large dataset, constraints helps us to quickly choose the right data structures.
