Dynamic programming (DP) is one of the most basic and, at the same time, challenging programming paradigms. This post covers its core concepts through two key strategies: memoization and tabulation. I use a series of representative problems (e.g., Fibonacci, Grid Traveler, and Can Sum) to demonstrate how these strategies simplify DP problems by breaking them down into smaller and reusable steps. The post includes detailed Java code examples for both approaches, guiding readers on how to solve classic algorithmic challenges efficiently.
This post is about competitive programming, a practice that tests both mental agility and algorithmic skills. Drawing from my personal experience, I cover the essentials, from mastering key algorithms to building an effective training plan. I share practical tips, resource recommendations, and strategies that helped me become a better contestant.
