Optimal Transport is a mathematical theory that focuses on finding the most efficient way to move resources from one location to another. It involves minimizing the cost associated with transporting goods, which can be represented as a cost function. This concept has applications in various fields, including economics, logistics, and machine learning. The theory was formalized by mathematicians like Gaspard Monge in the 18th century and has since evolved to include modern computational techniques. Optimal Transport can be used to compare probability distributions, making it valuable in areas such as image processing and data analysis, where it helps in aligning and transforming data sets.