Optimal Transport Theory is a mathematical framework that studies the most efficient ways to move resources from one location to another. It focuses on minimizing the cost associated with transporting goods, which can be represented as a distance or a monetary value. This theory has applications in various fields, including economics, logistics, and machine learning. The core idea involves finding the best way to match two distributions, such as probability distributions or mass distributions, while minimizing the total transportation cost. Techniques from calculus of variations and linear programming are often employed to solve these optimization problems effectively.