Nonlinear optimization is a mathematical process used to find the best solution for problems where the relationship between variables is not a straight line. Unlike linear optimization, which deals with linear equations, nonlinear optimization involves complex equations that can curve or bend. This makes it suitable for real-world scenarios, such as maximizing profits or minimizing costs, where factors interact in non-linear ways. In nonlinear optimization, the goal is to optimize an objective function subject to certain constraints. Techniques like gradient descent and Lagrange multipliers are often employed to navigate the solution space effectively. Applications can be found in fields such as engineering, economics, and machine learning.